P203 Subjective Perception and ERP Amplitude (the N170)

1 General introduction

1.1 Introduction

Linear Mixed Model were used.

1.2 Experiment design

• Independent variables:
• Stimulus Type (Type: intact vs. scrambled)
• Stimulus Category (Category: faces vs. houses)
• Hemisphere (Hemisphere: left vs. right)
• Duration (Duration: 17 vs. 200)

1.3 Preparations

## load libraries
library(tidyverse)
library(magrittr)
library(lme4)
library(lmerTest) 
library(emmeans)

# options for calculating estimated marginal means
# lmer.df = c("kenward-roger", "satterthwaite", "asymptotic")
emm_options(lmer.df = "satterthwaite", 
            lmerTest.limit = 1e6,
            opt.digits = FALSE)  # , pbkrtest.limit =, disable.pbkrtest = FALSE

set.seed(42)

# folders
folder_R <- "R"
folder_data <- "data"
folder_nesi <- "NeSI"
folder_bmm <- "bmmfit"

# load all the R files
sapply(list.files(folder_R, "*.R", full.names = TRUE, recursive = TRUE), source)

##         R/dummy_coding_P203.R R/general_theme.R R/geom_flat_violin.R R/get_pars.R R/qqplot_lmer.R R/sdiff_coding_P203.R R/substr_Event.R
## value   ?                     List,93           List,9               ?            ?               ?                     ?               
## visible FALSE                 FALSE             TRUE                 FALSE        FALSE           FALSE                 FALSE

# general setting
saveCSV <- FALSE
ratio_count <- 0.8
RT_min <- 200  # minimum response times

# set up the theme for plot and rainclound plot
plot_theme <- {
  general_theme +
    theme(legend.position = "right")
}

erp_theme <- {
  general_theme +
    theme(legend.position = "bottom")
}

ylimit_p1 <- c(2.5, -0.5)
ylimit_n170 <- c(1.5, -5.5)  # y axis for plotting N170 amplitude

resp_labels = c("sure faces", "not sure faces", "guessing", "not sure houses", "sure houses")

2 Hypotheses

Previous studies showed that the N170 amplitudes evoked by faces preseneted for longer durations were larger:

Duration <- c("17", "200")
Amplitudes <- c(-2.5, -5)
Hypotheses <- rep("means", 2)

df_lit <- data_frame(Duration, Amplitudes, Hypotheses)

plot_lit <- {
  ggplot(data = df_lit, aes(y = Amplitudes, x = Duration, fill = Duration)) +  # set the data, varialbes for x and y axises, and the variable for dividing data
    geom_col(color = "black") +
    facet_grid(. ~ Hypotheses) +
    scale_fill_manual(values = c(gray(.7), gray(.3))) +
    scale_y_reverse(limits =c(0, -5.5), breaks = seq(0, -5, -1), expand= c(0, 0)) +  # set the limit for y axis
    geom_hline(yintercept = -2.5, linetype = "dashed", color = "red", size = 1) +
    labs(x = "", y = expression(paste("Amplitudes (", mu, "V)"))) +  # set the names for main, x and y axises ,  title = "Means", 
    # theme_bw() +
    general_theme +
    theme( # strip.text = element_text(color = "white"),
      legend.position = "none",
      axis.line = element_line(size = 3, colour = "black"))
}

plot_lit

There are two possible explainations for the aggregated mean amplitudes:

Duration <- rep(c(rep("17", 5), rep("200", 5) ), 2)
Amplitudes <- c(c(rep(-2.5, 5), rep(-5, 5)), c(rep(-.83, 3), rep(-5, 7)))
TrialNum <- rep(as.character(rep(c(1:5), 2)), 2)
Hypotheses <- c(rep("Possibility 1", 10), rep("Possibility 2", 10))

df_st <- data_frame(Duration, Amplitudes, TrialNum, Hypotheses)

plot_st <- {
  ggplot(data = df_st, aes(y = Amplitudes, x = Duration, fill = Duration, color = TrialNum)) +  # set the data, varialbes for x and y axises, and the variable for dividing data
    geom_col(position = "dodge") +
    scale_fill_manual(values = c(gray(.7), gray(.3))) +
    scale_color_manual(values = rep("black", 5)) +
    facet_grid(. ~ Hypotheses) +
    geom_hline(yintercept = -2.5, linetype = "dashed", color = "red", size = 1) +
    scale_y_reverse(limits =c(0, -5.5), breaks = seq(0, -5, -1), expand= c(0, 0)) +  # set the limit for y axis
    labs(x = "Duration (ms)") +  # set the names for main, x and y axises , title = "At the single-trial level:", , y = expression(paste("Amplitudes (", mu, "V)"))
    theme_bw() +
    general_theme +
    theme(legend.position = "none",
          axis.title.y = element_blank(),
          axis.line = element_line(size = 3, colour = "black"))
}

plot_st

3 Brief introduction to the analysis

3.1 Equivalence tests

In this study, we do predict that there will be no differences between some conditions. For example, we expect that N170 amplitudes for faces presented for various duration are comparable as long as they are perceived with high subjective confidence. Thus, we employ Equivalence tests to explore if the differences are equivalent to a null region, which we defined as [-0.5, 0.5] in this study. Essentially, the equivalence test performs two one-sided tests to examine if the differences are smaller than the upper boundary of the region and larger than the lower boundary. By performing the conventional null hypothesis significance tests and the equivalence tests, there are (at least) five scenarios for the results:

# equivalence interval
delta_null <- 0.5  # the equivalent interval will be [-delta_null, delta_null]
CI_size = .8

df_equi <- tibble(
  Scenarios = paste("Scenario", 1:6),
  CI_low = c(delta_null-.15, -delta_null-.5, -delta_null+.15, delta_null-.6, -delta_null-.25, -delta_null-.1),
  CI_upp = CI_low + c(rep(CI_size, 5), 1.3)
) %>% 
  mutate(Scenarios = factor(Scenarios),
         Scenarios = factor(Scenarios, levels = rev(levels(Scenarios))))

gg_equi <- ggplot(df_equi, aes(xmin = CI_low, xmax = CI_upp, y = Scenarios)) +
  geom_errorbarh(height = .5, color = c("red", "red", "blue", rep("black", 3)), size = 1) + 
  geom_vline(xintercept = 0, linetype = "longdash") +
  geom_vline(xintercept = c(-delta_null, delta_null), linetype = "dashed", color = "gray30") +
  scale_x_continuous(limits = c(-1.15, 1.15), breaks = c(-delta_null, 0, delta_null)) +
  xlab(expression(paste("Amplitudes (", mu, "V)"))) +
  theme_bw() +
  theme(
    text = element_text(size = 10),
    axis.title = element_text(size = 16), 
    axis.text = element_text(size = 15), # the size of the texts in plot
    # axis.text.x = element_text(angle = 45, vjust = 0.5),
    legend.title=element_text(size=16),
    legend.text=element_text(size=16),
    # legend.position = "bottom",
    legend.key.width = unit(1.2, "cm"),
    plot.title = element_text(lineheight=.8, face="bold", size = 17),
    panel.border = element_blank(),
    panel.grid.minor = element_blank(),
    panel.grid.major = element_blank(),
    axis.line.x = element_line(colour = 'black', size=0.5, linetype='solid'),
    axis.line.y = element_line(colour = 'black', size=0.5, linetype='solid'),
    # remove the facet background color
    strip.text = element_text(face="bold", size=15, lineheight=5.0),
    strip.background = element_rect(fill="white", colour="white", size=1),
    strip.placement = "outside"
)

# ggsave("equivalence_test.png", gg_equi, width = 8, height = 4.7)

gg_equi

gg_equi_bw <- ggplot(df_equi, aes(xmin = CI_low, xmax = CI_upp, y = Scenarios)) +
  geom_errorbarh(height = .5, size = 1,
                 color = c("gray50", "gray50", "gray", rep("black", 3))) + 
  geom_vline(xintercept = 0, linetype = "longdash") +
  geom_vline(xintercept = c(-delta_null, delta_null), linetype = "dashed", color = "gray30") +
  scale_x_continuous(limits = c(-1.15, 1.15), breaks = c(-delta_null, 0, delta_null)) +
  xlab(expression(paste("Amplitudes (", mu, "V)"))) +
  theme_bw() +
  theme(
    text = element_text(size = 10),
    axis.title = element_text(size = 16), 
    axis.text = element_text(size = 15), # the size of the texts in plot
    # axis.text.x = element_text(angle = 45, vjust = 0.5),
    legend.title=element_text(size=16),
    legend.text=element_text(size=16),
    # legend.position = "bottom",
    legend.key.width = unit(1.2, "cm"),
    plot.title = element_text(lineheight=.8, face="bold", size = 17),
    panel.border = element_blank(),
    panel.grid.minor = element_blank(),
    panel.grid.major = element_blank(),
    axis.line.x = element_line(colour = 'black', size=0.5, linetype='solid'),
    axis.line.y = element_line(colour = 'black', size=0.5, linetype='solid'),
    # remove the facet background color
    strip.text = element_text(face="bold", size=15, lineheight=5.0),
    strip.background = element_rect(fill="white", colour="white", size=1),
    strip.placement = "outside"
)

# ggsave("equivalence_test_bw.png", gg_equi_pw, width = 8, height = 4.7)

gg_equi_bw

4 Behavior results

raw_beha_205 <- {
  "E205_35_Behavior.csv" %>% 
    file.path(folder_data, .) %>% 
    read_csv()
}

clean_beha_205 <- {
  raw_beha_205 %>%
    filter(`Procedure[Trial]` == "expProc") %>% # remove rows for practice and countdown
    select(ExperimentName, SubjCode = Subject, blockID, Block, Trial, Age, Sex, Handedness, Type = `stimCategory[Trial]`, Category = `FH[Trial]`, Duration = `stimDuration[Trial]`, ACC = `Resp.ACC[Trial]`, Resp.RT = `Resp.RT[Trial]`, Resp = `Resp.RESP[Trial]`, Stimuli = `stimName[Trial]`) %>% # select and rename the columns (remove unuseful columns)
    mutate(
      SubjCode = factor(paste("P", SubjCode, sep = "")), # save SubjCode as factor
      Type = substr(Type, 1, 1),
      Type = recode_factor(Type, "N" = "intact", "S" = "scrambled"), # rename the levels of Type
      Category = recode(Category, "hosue" = "house", "house" = "house", "face" = "face", .default = "face"), # rename "hosue" as "house"
      Duration = factor(Duration), # save Duration as factor
      Category = as.factor(Category),
      Resp = as.factor(Resp),
      blockID = as.factor(blockID)
    ) %>% 
    group_by(SubjCode, Type, Category, Duration, ACC) %>% # divide the data based on these conditions
    mutate(
      RT = Resp.RT + as.numeric(levels(Duration))[Duration],
      RT.Z = scale(RT), # calculate the Z value for reaction times within each group
      RT.Within3Z = ifelse(RT.Z <= 3 & RT.Z >= -3, 1, ifelse(RT.Z < -3 | RT.Z > 3, 0, NaN)) # if the Z values are between -3 and 3, will be marked as 1
    ) %>% 
    ungroup() # ungroup the data 
  
}

# behavior.tidyup
head(clean_beha_205, 10)

## # A tibble: 10 x 18
##    ExperimentName SubjCode blockID Block Trial   Age Sex   Handedness Type      Category Duration   ACC Resp.RT Resp  Stimuli       RT RT.Z[,1] RT.Within3Z[,1]
##    <chr>          <fct>    <fct>   <dbl> <dbl> <dbl> <chr> <chr>      <fct>     <fct>    <fct>    <dbl>   <dbl> <fct> <chr>      <dbl>    <dbl>           <dbl>
##  1 205_Guessing   P501     1          33     6    25 male  right      intact    face     17           1     989 1     NfM153.bmp  1006   1.87                 1
##  2 205_Guessing   P501     1          33     7    25 male  right      scrambled house    17           0    2429 4     Shn150.bmp  2446   1.11                 1
##  3 205_Guessing   P501     1          33     8    25 male  right      intact    house    17           0    1393 4     Nhn212.bmp  1410   0.427                1
##  4 205_Guessing   P501     1          33     9    25 male  right      intact    house    17           0    1270 4     Nhn040.bmp  1287   0.275                1
##  5 205_Guessing   P501     1          33    10    25 male  right      intact    house    17           0     899 4     Nhn227.bmp   916  -0.185                1
##  6 205_Guessing   P501     1          33    11    25 male  right      intact    house    17           0    1085 3     Nhn198.bmp  1102   0.0454               1
##  7 205_Guessing   P501     1          33    12    25 male  right      intact    face     17           1     547 1     NfF112.bmp   564  -0.380                1
##  8 205_Guessing   P501     1          33    13    25 male  right      intact    house    17           1    1434 5     Nhn229.bmp  1451   1.79                 1
##  9 205_Guessing   P501     1          33    14    25 male  right      scrambled face     17           0     837 4     SfM153.bmp   854  -0.261                1
## 10 205_Guessing   P501     1          33    15    25 male  right      intact    face     17           1     567 1     NfM146.bmp   584  -0.278                1

# check the number of trials for 205
sum.count.205 <- {
  clean_beha_205 %>% 
    group_by(SubjCode, Type, Category, Duration) %>% 
    summarize(Count_sum = n())
}

valid.count.205 <- {
  clean_beha_205 %>% 
    filter(RT > RT_min) %>% 
    group_by(SubjCode, Type, Category, Duration) %>% 
    summarize(Count = n()) %>% 
    right_join(sum.count.205, by = c("SubjCode", "Type", "Category", "Duration")) %>% 
    mutate(ratio = Count / Count_sum) %>% 
    filter(ratio > ratio_count) %$%
    SubjCode %>% 
    unique()
}

# remove the participants
clean_beha_205 %<>% filter(SubjCode %in% valid.count.205)

# remove participants whose performance in Key 1 was lower than 95%
valid.17 <- {
  clean_beha_205 %>%
    filter(Duration == 17, Resp == 1) %>% # Type == "intact",
    group_by(SubjCode, Duration, Resp) %>% # Type, 
    summarize(Accuracy = mean(ACC), Count = n()) %>% 
    filter(Accuracy > 0.95) %$%
    SubjCode
}

# clean_beha_205 %>%
#   filter(Duration == 17, Resp == 1) %>% # Type == "intact",
#   group_by(SubjCode, Duration, Resp) %>% # Type, 
#   summarize(Accuracy = mean(ACC), Count = n()) %>% 
#   filter(Accuracy < 0.95) %>% 
#   ungroup() %>% 
#   summarize(mean(Accuracy), min(Accuracy), max(Accuracy))  

clean_beha_205 %<>% filter(SubjCode %in% valid.17)

4.1 Participant demographic information

subj.info.205 <- {
  clean_beha_205 %>% 
    select(SubjCode, Age, Sex, Handedness) %>% # select the columns of participant inforamtion
    distinct() # only keep the unique rows
}

subj.info.205

## # A tibble: 28 x 4
##    SubjCode   Age Sex    Handedness
##    <fct>    <dbl> <chr>  <chr>     
##  1 P504        22 male   right     
##  2 P506        25 female right     
##  3 P507        25 female right     
##  4 P508        22 male   left      
##  5 P510        22 male   right     
##  6 P511        20 female right     
##  7 P512        30 female right     
##  8 P513        29 male   right     
##  9 P514        32 female right     
## 10 P515        28 male   left      
## # … with 18 more rows

mean(subj.info.205$Age)

## [1] 22.32143

range(subj.info.205$Age)

## [1] 18 32

sum(subj.info.205$Sex == "female")

## [1] 20

4.2 Allocation of responses in each condition

tmp.count <- {
  clean_beha_205 %>% 
    group_by(SubjCode, Type, Category, Duration) %>% 
    summarize(Count_sum = n())
}

avg.dist.long.205 <- {
  clean_beha_205 %>% 
    # mutate(RespRateFace = if_else(Category == "face", ACC, if_else(Category == "house", as.numeric(!ACC), NaN))) %>% 
    group_by(SubjCode, Type, Category, Duration, Resp) %>% 
    summarize(Count = n()) %>% 
    
    right_join(tmp.count, by = c("SubjCode", "Type", "Category", "Duration")) %>% 
    mutate(RespRate = Count / Count_sum) %>% 
    ungroup()
}

# descriptive statistics of accuracy for plotting
desc.dist.205 <- {
  avg.dist.long.205 %>%
    group_by(Type, Category, Duration, Resp) %>%
    summarize(Mean = mean(RespRate)
              , N = n()
              , SD = if_else(N > 1, sd(RespRate), 0)
              , SE = SD/sqrt(N)
              , SE.lo = Mean - SE, SE.hi = Mean + SE
              , CI_lo = Mean - SE * 1.96
              , CI_hi = Mean + SE * 1.96
              , Median = median(RespRate)
              , Lower = Mean - SD, Upper = Mean + SD # for rainclound plot
    ) %>% 
    ungroup() %>% 
    add_row(Type = "intact", Category = "house", Duration = "200", Resp = "2", Mean = 0, N = 0, SD = 0, SE = 0, SE.lo = 0,
            SE.hi = 0, CI_lo = 0, CI_hi = 0, Median = 0, Lower = 0, Upper = 0, .before = 17)
}

avg.dist.wide.205 <- {
  avg.dist.long.205 %>% 
    mutate(Conditions = paste(Type, Category, Duration, Resp, sep = "_")) %>% 
    select(SubjCode, Conditions, RespRate) %>% 
    spread(Conditions, RespRate)
}

desc.dist.205

## # A tibble: 40 x 15
##    Type   Category Duration Resp     Mean     N     SD      SE   SE.lo  SE.hi   CI_lo  CI_hi  Median     Lower  Upper
##    <chr>  <chr>    <chr>    <chr>   <dbl> <dbl>  <dbl>   <dbl>   <dbl>  <dbl>   <dbl>  <dbl>   <dbl>     <dbl>  <dbl>
##  1 intact face     17       1     0.469      28 0.331  0.0625  0.406   0.531  0.346   0.591  0.447    0.138    0.799 
##  2 intact face     17       2     0.321      28 0.228  0.0430  0.278   0.364  0.236   0.405  0.347    0.0929   0.548 
##  3 intact face     17       3     0.159      28 0.163  0.0308  0.128   0.190  0.0988  0.220  0.0969  -0.00385  0.322 
##  4 intact face     17       4     0.0478     28 0.0565 0.0107  0.0371  0.0584 0.0268  0.0687 0.0219  -0.00873  0.104 
##  5 intact face     17       5     0.00859    12 0.0129 0.00371 0.00488 0.0123 0.00132 0.0159 0.00312 -0.00427  0.0215
##  6 intact face     200      1     0.977      28 0.0320 0.00606 0.971   0.983  0.965   0.989  0.988    0.945    1.01  
##  7 intact face     200      2     0.04       10 0.0403 0.0127  0.0273  0.0527 0.0150  0.0650 0.0188  -0.000311 0.0803
##  8 intact face     200      3     0.0208      9 0.0125 0.00417 0.0167  0.025  0.0127  0.029  0.0125   0.00833  0.0333
##  9 intact face     200      4     0.0125      1 0      0       0.0125  0.0125 0.0125  0.0125 0.0125   0.0125   0.0125
## 10 intact face     200      5     0.0125      4 0      0       0.0125  0.0125 0.0125  0.0125 0.0125   0.0125   0.0125
## # … with 30 more rows

dist.RainPlot.205 <- {
  ggplot(data = avg.dist.long.205, aes(y = RespRate, x = as.factor(Resp), fill = Category)) + 
    geom_flat_violin(position = position_nudge(x = .2, y = 0), alpha = .7) +
    geom_point(aes(y = RespRate, color = Category), position = position_jitter(width = .15), size = .5, alpha = .8) +
    geom_boxplot(aes(y = RespRate), width = .2, outlier.shape = NA, alpha = .7) +
    geom_point(data = desc.dist.205, aes(y = Mean, x = Resp, color = Category), position = position_nudge(x = 0.3), size = 2.5) +
    geom_errorbar(data = desc.dist.205, aes(y = Mean, ymin = Lower, ymax = Upper), position = position_nudge(x = 0.3), width = 0) +
    facet_grid(Type ~ Duration) +
    
    # facet_grid(. ~ Type, switch = "x") +  # create two panels to show data
    # scale_colour_grey() + # start = .1, end = .6, color for the contour
    # scale_fill_grey() + # start = .3, end = .6, color for the fill
    # scale_color_brewer(palette = "Set1") + # palette = "RdBu"
    # scale_fill_brewer(palette = "Set1") + # palette = "RdBu"
    labs(title = "Reaction times for 205", x = "Stimulus Type", y = "Reaction times (ms)", fill = "Duration(ms)", color = "Duration(ms)") +  # set the names for main, x and y axises and the legend
    # scale_x_discrete(labels=resp_labels) +
    # coord_cartesian(ylim = c(0, 1.05)) +  # set the limit for y axis
    # scale_y_continuous(expand= c(0, 0)) +  # remove the space between columns and x axis
    theme_bw() + # the backgroud color
    plot_theme
}

 

dura.labels <- c("17ms", "200ms")
names(dura.labels) <- c(17, 200)

dist.ColuPlot.205 <- {
  ggplot(data = desc.dist.205, aes(y = Mean, x = Resp, fill = Category)) +  # set the data, varialbes for x and y axises, and the variable for dividing data
    geom_col(position = "dodge", color = "black", alpha = .7) +  # position of columns and countour of columns
    facet_grid(Type ~ Duration,
               labeller = labeller(Duration = dura.labels)) +
    geom_errorbar(mapping = aes(ymin = CI_lo, ymax = CI_hi), linetype = 1,  # set the error bar
                  show.legend = FALSE, width = 0.25, alpha = .5,
                  position = position_dodge(width=0.9)) +
    geom_hline(yintercept = c(1), linetype = 5, alpha = 0.5) +  # add the line for 0.5 and 1 (y)
    coord_cartesian(ylim = c(0,1.1)) +  # set the limit for y axis
    scale_y_continuous(expand= c(0, 0)) +  # remove the space between columns and x axis
    labs(title = "", x = "Responses", y = "Percentage", fill = "Category") +  # set the names for main, x and y axises Subjective Responses for E205
    scale_fill_grey() +  # set the color for columns
    # geom_text(label = c("***", "***", "", ""), color = c("red", "red", "red", "red"), size = 6, nudge_y = 0.05, nudge_x = rep(c(-0.225, 0.225), 2)) + # add starts to the significant columns
    erp_theme
}

dist.ColuPlot.205

4.3 Percentage of face stimuli

df_ratio_205 <- {
  clean_beha_205 %>%
    mutate(isFace = if_else(Category == "face", 1, 0)) %>%
    group_by(SubjCode, Type, Duration, Resp) %>%
    summarize(ratio = mean(isFace), Count = n())
}

desc_ratio_205 <- {
  df_ratio_205 %>%
    group_by(Type, Duration, Resp) %>%
    summarize(Mean = mean(ratio)
              , N = n()
              , SD = if_else(N > 1, sd(ratio), 0)
              , SE = SD/sqrt(N)
              , SE.lo = Mean - SE, SE.hi = Mean + SE
              , CI_lo = Mean - SE * 1.96
              , CI_hi = Mean + SE * 1.96
              , Median = median(ratio)
              , Lower = Mean - SD, Upper = Mean + SD # for rainclound plot
    ) %>%
    ungroup()
}

rate.ColuPlot.205 <- {
  ggplot(data = desc_ratio_205, aes(y = Mean, x = Resp)) +  # set the data, varialbes for x and y axises, and the variable for dividing data
    geom_col(position = "dodge", color = "black", alpha = .7) +  # position of columns and countour of columns
    facet_grid(Type ~ Duration, 
               labeller = labeller(Duration = dura.labels)) +
    geom_errorbar(mapping = aes(ymin = SE.lo, ymax = SE.hi), linetype = 1,  # set the error bar
                  show.legend = FALSE, width = 0.25, alpha = .5,
                  position = position_dodge(width=0.9)) +
    geom_hline(yintercept = c(0.5, 1), linetype = 5, alpha = 0.5) +  # add the line for 0.5 and 1 (y)
    # coord_cartesian(ylim = c(0,1.1)) +  # set the limit for y axis
    scale_y_continuous(expand= c(0, 0), limits = c(-0.1, 1.15), breaks = seq(0, 1, 0.25)) +  # remove the space between columns and x axis
    labs(x = "Responses", y = "Percentage") +  # set the names for main, x and y axises Subjective Responses for E205
    scale_fill_grey() +  # set the color for columns
    # geom_text(label = c("***", "***", "", ""), color = c("red", "red", "red", "red"), size = 6, nudge_y = 0.05, nudge_x = rep(c(-0.225, 0.225), 2)) + # add starts to the significant columns
    plot_theme
}

rate.ColuPlot.205

Combine the two plots (for publication):

# combine the two behavior response plots

dist_ColuPlot_205 <- 
  ggplot(data = desc.dist.205, aes(y = Mean, x = Resp, fill = Category)) +  # set the data, varialbes for x and y axises, and the variable for dividing data
  geom_col(position = "dodge", color = "black", alpha = .7) +  # position of columns and countour of columns
  facet_grid(Type ~ Duration,
             labeller = labeller(Duration = dura.labels)) +
  geom_errorbar(mapping = aes(ymin = CI_lo, ymax = CI_hi), linetype = 1,  # set the error bar
                show.legend = FALSE, width = 0.25, alpha = .5,
                position = position_dodge(width=0.9)) +
  geom_hline(yintercept = c(1), linetype = 5, alpha = 0.5) +  # add the line for 0.5 and 1 (y)
  # coord_cartesian(ylim = c(0,1.1)) +  # set the limit for y axis
  scale_y_continuous(expand= c(0, 0), limits = c(-0.1, 1.15), breaks = seq(0, 1, 0.25)) +# remove the space between columns and x axis  
  labs(x = "Responses", y = "Percentage", fill = "Category") +  # set the names for main, x and y axises Subjective Responses for E205
  scale_fill_grey() +  # set the color for columns
  # geom_text(label = c("***", "***", "", ""), color = c("red", "red", "red", "red"), size = 6, nudge_y = 0.05, nudge_x = rep(c(-0.225, 0.225), 2)) + # add starts to the significant columns
  erp_theme +
  theme(legend.position = c(0.53, 0.3)) 

resp_ColuPlot_205 <- {
  ggplot(data = desc_ratio_205, aes(y = Mean, x = Resp)) +  # set the data, varialbes for x and y axises, and the variable for dividing data
    geom_col(position = "dodge", color = "black", alpha = .7) +  # position of columns and countour of columns
    facet_grid(Type ~ Duration, 
               labeller = labeller(Duration = dura.labels)) +
    geom_errorbar(mapping = aes(ymin = SE.lo, ymax = SE.hi), linetype = 1,  # set the error bar
                  show.legend = FALSE, width = 0.25, alpha = .5,
                  position = position_dodge(width=0.9)) +
    geom_hline(yintercept = c(0.5, 1), linetype = 5, alpha = 0.5) +  # add the line for 0.5 and 1 (y)
    # coord_cartesian(ylim = c(0,1.1)) +  # set the limit for y axis
    scale_y_continuous(expand= c(0, 0), limits = c(-0.1, 1.15), breaks = seq(0, 1, 0.25)) +  # remove the space between columns and x axis
    labs(x = "Responses", y = "Percentage") +  # set the names for main, x and y axises Subjective Responses for E205
    scale_fill_grey() +  # set the color for columns
    # geom_text(label = c("***", "***", "", ""), color = c("red", "red", "red", "red"), size = 6, nudge_y = 0.05, nudge_x = rep(c(-0.225, 0.225), 2)) + # add starts to the significant columns
    plot_theme
}
  
plot_two_response <- ggpubr::ggarrange(dist_ColuPlot_205, resp_ColuPlot_205, nrow = 1,
            widths = c(1, .7),
            labels = c("A", "B"),
            font.label = list(size = 24))  # ratio between the two subplots)

# ggsave('plot_two_response.pdf', plot_two_response, width = 10, height = 5)

5 ERP amplitude analysis

5.1 Load mean amplitude for single trials

The raw mean amplitude of each trial for all participants were loaded.

# read the trial mean amplitude data
df.erp.E2 <- {
  "E205_MeanAmp.csv" %>% 
    file.path(folder_data, .) %>% 
    read_csv() %>% 
    substr_Event() %>% 
    filter(SubjCode %in% valid.17) %>% 
    mutate(SubjCode = as.factor(SubjCode),
           Hemisphere = as.factor(Hemisphere),
           urResponse = as.factor(urResponse),
           Stimuli = substr(stimName, 2, 6))
}

df.erp.E2 %>% 
  group_by(SubjCode, Stimuli, Hemisphere, Type, Category, Duration) %>% 
  summarize(count = n())

## # A tibble: 17,834 x 7
## # Groups:   SubjCode, Stimuli, Hemisphere, Type, Category [8,960]
##    SubjCode Stimuli Hemisphere Type      Category Duration count
##    <fct>    <chr>   <fct>      <fct>     <fct>    <fct>    <int>
##  1 P504     fF002   Left       intact    face     17          14
##  2 P504     fF002   Left       intact    face     200          4
##  3 P504     fF002   Left       scrambled face     17           8
##  4 P504     fF002   Left       scrambled face     200          4
##  5 P504     fF002   Right      intact    face     17          14
##  6 P504     fF002   Right      intact    face     200          4
##  7 P504     fF002   Right      scrambled face     17           8
##  8 P504     fF002   Right      scrambled face     200          4
##  9 P504     fF015   Left       intact    face     17          16
## 10 P504     fF015   Left       intact    face     200          4
## # … with 17,824 more rows

5.2 Linear mixed model across responses

5.2.1 Set the successive difference coding

# dummy coding
df.erp.E2.all <- sdif_coding_P203_erp(df.erp.E2)

5.2.2 Amplitudes of the P1

# only keep the data for amplitudes of the P1 (correct and incorrect erps)
df.erp.P1.E2 <- {
  df.erp.E2.all %>% 
    filter(Component == "P1")
}

if (saveCSV) {
  output.erp.P1.E2 <- file.path(folder_nesi, "E205_erp_P1.RData")
  save(df.erp.P1.E2, file = output.erp.P1.E2)
}

# the structure of this dataset
head(df.erp.P1.E2, 10)

## # A tibble: 10 x 64
##    Channel Event Urevent TrialNumber Block_ST_table Response urResponse    RT  urRT Reject Rejthresh Rejjp SubjCode ChanCent ChanCluster_1 ChanCluster_2 ChanCluster_3 ChanCluster_4 ChanCluster_5 ChanCluster_6 ChanCluster_7 Hemisphere Component MeanAmp MethodCode Method        IsPositive StartFrame PeakFrame EndFrame StartTime PeakTime EndTime FrameSize WindowSize StartAmplitude PeakAmplitude EndAmplitude Session Block_beha_table Trial Resp_ACC Resp_OnsetDelay Resp_RT stimName   Type   Category Duration Stimuli Type_C Cate_C Dura_C Type_Cate Type_Dura Cate_Dura Type_Cate_Dura Hemi_C Type_Hemi Cate_Hemi Dura_Hemi Type_Cate_Hemi Type_Dura_Hemi Cate_Dura_Hemi Type_Cate_Dura_Hemi
##    <chr>   <chr>   <dbl>       <dbl>          <dbl>    <dbl> <fct>      <dbl> <dbl>  <dbl>     <dbl> <dbl> <fct>    <chr>    <chr>         <chr>         <chr>         <chr>         <chr>         <chr>         <chr>         <fct>      <chr>       <dbl>      <dbl> <chr>              <dbl>      <dbl>     <dbl>    <dbl>     <dbl>    <dbl>   <dbl>     <dbl>      <dbl>          <dbl>         <dbl>        <dbl>   <dbl>            <dbl> <dbl>    <dbl>           <dbl>   <dbl> <chr>      <fct>  <fct>    <fct>    <chr>    <dbl>  <dbl>  <dbl>     <dbl>     <dbl>     <dbl>          <dbl>  <dbl>     <dbl>     <dbl>     <dbl>          <dbl>          <dbl>          <dbl>               <dbl>
##  1 E65     NF2+     2590         646              2       11 RE11         706   756      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       P1         -1.08           2 MeanAmplitude          1         24        29       34        96      116     136        10         40         -5.91           2.99       -4.57        1               35    11        1              16     540 NfF040.bmp intact face     200      fF040     -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625
##  2 E65     NF2+     2610         651              2       11 RE11         564   614      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       P1          2.91           2 MeanAmplitude          1         24        34       34        96      136     136        10         40          1.44           7.07        7.07        1               35    16        1              17     397 NfF078.bmp intact face     200      fF078     -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625
##  3 E65     NF2+     2622         654              2       11 RE11         574   624      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       P1          2.35           2 MeanAmplitude          1         24        27       34        96      108     136        10         40         -1.17           5.15        3.13        1               35    19        1              16     408 NfM122.bmp intact face     200      fM122     -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625
##  4 E65     NF2+     2626         655              2       11 RE11         387   437      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       P1         10.7            2 MeanAmplitude          1         24        25       34        96      100     136        10         40         11.5           12.1         9.94        1               35    20        1              17     220 NfM013.bmp intact face     200      fM013     -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625
##  5 E65     NF2+     2666         665              2       11 RE11         361   411      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       P1          0.608          2 MeanAmplitude          1         24        28       34        96      112     136        10         40         -5.60           4.06        0.541       1               35    30        1              16     195 NfM142.bmp intact face     200      fM142     -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625
##  6 E65     NF2+     2686         670              2       11 RE11         366   416      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       P1         -1.54           2 MeanAmplitude          1         24        29       34        96      116     136        10         40         -6.76           1.08       -1.13        1               35    35        1              16     200 NfF112.bmp intact face     200      fF112     -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625
##  7 E65     NF2+     2690         671              2       11 RE11         423   473      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       P1          3.35           2 MeanAmplitude          1         24        30       34        96      120     136        10         40          1.09           7.85       -0.560       1               35    36        1              17     256 NfF017.bmp intact face     200      fF017     -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625
##  8 E65     NF2+     2722         679              2       11 RE11         446   496      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       P1          6.01           2 MeanAmplitude          1         24        34       34        96      136     136        10         40          0.630          9.21        9.21        1               35    44        1              17     279 NfF068.bmp intact face     200      fF068     -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625
##  9 E65     NF2+     2778         693              2       11 RE11         454   504      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       P1          2.87           2 MeanAmplitude          1         24        31       34        96      124     136        10         40         -2.85           6.41        1.56        1               35    58        1              16     288 NfF076.bmp intact face     200      fF076     -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625
## 10 E65     NF2+     2790         696              2       11 RE11         441   491      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       P1          0.793          2 MeanAmplitude          1         24        28       34        96      112     136        10         40          0.139          2.21        0.914       1               35    61        1              16     275 NfF079.bmp intact face     200      fF079     -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625

5.2.2.1 The maximal model

file.max.P1.E2 <- file.path(folder_nesi, "E205_P1_lmm_max.RData")

# fit the maximal model
if (!file.exists(file.max.P1.E2)) {
  lmm.max.P1.E2 <- lmer(MeanAmp ~ Type * Category * Duration * Hemisphere +
                          (1 + Type_C + Cate_C + Dura_C + Hemi_C +
                             Type_Cate + Type_Dura + Cate_Dura + Type_Hemi + Cate_Hemi + Dura_Hemi +
                             Type_Cate_Dura + Type_Cate_Hemi + Type_Dura_Hemi + Cate_Dura_Hemi +
                             Type_Cate_Dura_Hemi
                           | SubjCode) +
                          (1 + Type_C + Dura_C + Hemi_C +
                             Type_Dura + Type_Hemi + Dura_Hemi +
                             Type_Dura_Hemi
                           | Stimuli),
                        data = df.erp.P1.E2,
                        verbose = TRUE,
                        control = lmerControl(optimizer = "bobyqa",  # nloptwrap Nelder_Mead
                                              optCtrl = list(maxfun = 1e7)))
} else {
  load(file.max.P1.E2)
}

print(summary(lmm.max.P1.E2), corr = FALSE)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Type * Category * Duration * Hemisphere + (1 + Type_C +      Cate_C + Dura_C + Hemi_C + Type_Cate + Type_Dura + Cate_Dura +      Type_Hemi + Cate_Hemi + Dura_Hemi + Type_Cate_Dura + Type_Cate_Hemi +      Type_Dura_Hemi + Cate_Dura_Hemi + Type_Cate_Dura_Hemi | SubjCode) +      (1 + Type_C + Dura_C + Hemi_C + Type_Dura + Type_Hemi + Dura_Hemi +          Type_Dura_Hemi | Stimuli)
##    Data: df.erp.P1.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+07))
## 
## REML criterion at convergence: 399168.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.4269 -0.5739 -0.0116  0.5710  9.2481 
## 
## Random effects:
##  Groups   Name                Variance  Std.Dev. Corr                                                                                     
##  Stimuli  (Intercept)          0.020242 0.14228                                                                                           
##           Type_C               0.071284 0.26699   0.05                                                                                    
##           Dura_C               0.072281 0.26885   0.40 -0.05                                                                              
##           Hemi_C               0.001384 0.03720   0.70  0.19  0.70                                                                        
##           Type_Dura            0.421342 0.64911  -0.10  0.67  0.12 -0.21                                                                  
##           Type_Hemi            0.011305 0.10632  -0.46  0.71  0.02  0.14  0.32                                                            
##           Dura_Hemi            0.002092 0.04574   0.49  0.68  0.57  0.49  0.73  0.25                                                      
##           Type_Dura_Hemi       0.075770 0.27526  -0.34  0.77  0.25  0.03  0.79  0.80  0.63                                                
##  SubjCode (Intercept)          1.797320 1.34064                                                                                           
##           Type_C               0.113610 0.33706   0.10                                                                                    
##           Cate_C               0.050124 0.22388  -0.36 -0.29                                                                              
##           Dura_C               0.321549 0.56705   0.44 -0.09 -0.36                                                                        
##           Hemi_C               0.594791 0.77123   0.31  0.11  0.48 -0.18                                                                  
##           Type_Cate            0.378197 0.61498   0.67  0.25 -0.36  0.38  0.10                                                            
##           Type_Dura            0.336157 0.57979   0.05  0.22  0.05  0.29  0.02  0.41                                                      
##           Cate_Dura            0.043316 0.20813   0.54 -0.40  0.09  0.18  0.55  0.41 -0.19                                                
##           Type_Hemi            0.034810 0.18657   0.02  0.47 -0.17 -0.02 -0.35 -0.27 -0.25 -0.45                                          
##           Cate_Hemi            0.011529 0.10737   0.00  0.55  0.23 -0.23 -0.06  0.03  0.17 -0.61  0.51                                    
##           Dura_Hemi            0.239585 0.48947   0.19 -0.08 -0.46  0.46 -0.37  0.23  0.19  0.27  0.22 -0.50                              
##           Type_Cate_Dura       0.382269 0.61828   0.70  0.29 -0.67  0.53 -0.34  0.77  0.23  0.03  0.19  0.22  0.32                        
##           Type_Cate_Hemi       0.127146 0.35658   0.00 -0.38  0.04  0.27  0.07  0.52  0.45  0.46 -0.83 -0.52  0.20  0.06                  
##           Type_Dura_Hemi       0.133551 0.36545  -0.48  0.43  0.55 -0.36  0.34 -0.39  0.23 -0.26  0.30  0.29 -0.07 -0.59 -0.26            
##           Cate_Dura_Hemi       0.074409 0.27278   0.06 -0.44  0.18  0.21 -0.41 -0.16 -0.04  0.05  0.46  0.04  0.50  0.10 -0.18 -0.03      
##           Type_Cate_Dura_Hemi  0.470022 0.68558   0.17 -0.64  0.44  0.34 -0.05  0.17  0.28  0.38 -0.16 -0.10  0.30  0.07  0.38 -0.12  0.75
##  Residual                     15.678168 3.95957                                                                                           
## Number of obs: 71282, groups:  Stimuli, 80; SubjCode, 30
## 
## Fixed effects:
##                                                 Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)                                     1.262713   0.245906  29.244440   5.135  1.7e-05 ***
## Type2-1                                        -0.106661   0.076825  37.967608  -1.388  0.17312    
## Category2-1                                     0.085615   0.062504  46.064875   1.370  0.17741    
## Duration2-1                                     0.353467   0.113340  33.150333   3.119  0.00374 ** 
## Hemisphere2-1                                   0.117784   0.145144  29.038601   0.811  0.42368    
## Type2-1:Category2-1                             0.122340   0.145142  38.767656   0.843  0.40445    
## Type2-1:Duration2-1                             0.313789   0.146180  44.709815   2.147  0.03729 *  
## Category2-1:Duration2-1                         0.122743   0.099766  60.821395   1.230  0.22332    
## Type2-1:Hemisphere2-1                           0.011121   0.078696  72.888288   0.141  0.88801    
## Category2-1:Hemisphere2-1                      -0.041851   0.073108 178.077600  -0.572  0.56774    
## Duration2-1:Hemisphere2-1                       0.005918   0.113598  30.243456   0.052  0.95879    
## Type2-1:Category2-1:Duration2-1                 0.008769   0.231065  63.040636   0.038  0.96985    
## Type2-1:Category2-1:Hemisphere2-1               0.190520   0.156105  79.923915   1.220  0.22588    
## Type2-1:Duration2-1:Hemisphere2-1              -0.044562   0.158002  76.945281  -0.282  0.77867    
## Category2-1:Duration2-1:Hemisphere2-1          -0.151587   0.148829 122.513631  -1.019  0.31043    
## Type2-1:Category2-1:Duration2-1:Hemisphere2-1   0.123037   0.312596  84.251373   0.394  0.69487    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular

5.2.2.2 The zero-correlation-parameter model

file.zcp.P1.E2 <- file.path(folder_nesi, "E205_P1_lmm_zcp.RData")

# fit the zcp model
if (!file.exists(file.zcp.P1.E2)) {
  lmm.zcp.P1.E2 <- lmer(MeanAmp ~ Type * Category * Duration * Hemisphere + 
                          (1 + Type_C + Cate_C + Dura_C + Hemi_C +  
                             Type_Cate + Type_Dura + Cate_Dura + Type_Hemi + Cate_Hemi + Dura_Hemi +
                             Type_Cate_Dura + Type_Cate_Hemi + Type_Dura_Hemi + Cate_Dura_Hemi + 
                             Type_Cate_Dura_Hemi
                           || SubjCode) +
                          (1 + Type_C + Dura_C + Hemi_C + 
                             Type_Dura + Type_Hemi + Dura_Hemi +
                             Type_Dura_Hemi 
                           || Stimuli),
                        data = df.erp.P1.E2,
                        verbose = TRUE,
                        control = lmerControl(optimizer = "bobyqa",  # nloptwrap Nelder_Mead
                                              optCtrl = list(maxfun = 1e7)))
} else {
  load(file.zcp.P1.E2)
}

print(summary(lmm.zcp.P1.E2), corr = FALSE)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Type * Category * Duration * Hemisphere + (1 + Type_C +      Cate_C + Dura_C + Hemi_C + Type_Cate + Type_Dura + Cate_Dura +      Type_Hemi + Cate_Hemi + Dura_Hemi + Type_Cate_Dura + Type_Cate_Hemi +      Type_Dura_Hemi + Cate_Dura_Hemi + Type_Cate_Dura_Hemi ||      SubjCode) + (1 + Type_C + Dura_C + Hemi_C + Type_Dura + Type_Hemi +      Dura_Hemi + Type_Dura_Hemi || Stimuli)
##    Data: df.erp.P1.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+07))
## 
## REML criterion at convergence: 399290.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.5143 -0.5726 -0.0123  0.5716  9.3014 
## 
## Random effects:
##  Groups      Name                Variance  Std.Dev. 
##  Stimuli     Type_Dura_Hemi      1.932e-09 4.395e-05
##  Stimuli.1   Dura_Hemi           2.685e-13 5.181e-07
##  Stimuli.2   Type_Hemi           4.559e-13 6.752e-07
##  Stimuli.3   Type_Dura           2.496e-01 4.996e-01
##  Stimuli.4   Hemi_C              0.000e+00 0.000e+00
##  Stimuli.5   Dura_C              5.021e-02 2.241e-01
##  Stimuli.6   Type_C              3.631e-02 1.906e-01
##  Stimuli.7   (Intercept)         1.472e-02 1.213e-01
##  SubjCode    Type_Cate_Dura_Hemi 3.519e-12 1.876e-06
##  SubjCode.1  Cate_Dura_Hemi      1.916e-13 4.377e-07
##  SubjCode.2  Type_Dura_Hemi      0.000e+00 0.000e+00
##  SubjCode.3  Type_Cate_Hemi      0.000e+00 0.000e+00
##  SubjCode.4  Type_Cate_Dura      0.000e+00 0.000e+00
##  SubjCode.5  Dura_Hemi           2.525e-01 5.025e-01
##  SubjCode.6  Cate_Hemi           0.000e+00 0.000e+00
##  SubjCode.7  Type_Hemi           0.000e+00 0.000e+00
##  SubjCode.8  Cate_Dura           3.003e-02 1.733e-01
##  SubjCode.9  Type_Dura           3.098e-01 5.566e-01
##  SubjCode.10 Type_Cate           2.762e-01 5.255e-01
##  SubjCode.11 Hemi_C              6.411e-01 8.007e-01
##  SubjCode.12 Dura_C              3.127e-01 5.592e-01
##  SubjCode.13 Cate_C              4.097e-02 2.024e-01
##  SubjCode.14 Type_C              1.055e-01 3.248e-01
##  SubjCode.15 (Intercept)         1.781e+00 1.334e+00
##  Residual                        1.570e+01 3.963e+00
## Number of obs: 71282, groups:  Stimuli, 80; SubjCode, 30
## 
## Fixed effects:
##                                                 Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)                                    1.263e+00  2.446e-01  2.918e+01   5.162 1.59e-05 ***
## Type2-1                                       -1.068e-01  7.208e-02  3.472e+01  -1.482   0.1474    
## Category2-1                                    8.635e-02  5.768e-02  4.827e+01   1.497   0.1409    
## Duration2-1                                    3.532e-01  1.108e-01  3.198e+01   3.187   0.0032 ** 
## Hemisphere2-1                                  1.172e-01  1.503e-01  2.930e+01   0.780   0.4419    
## Type2-1:Category2-1                            1.227e-01  1.262e-01  4.055e+01   0.972   0.3366    
## Type2-1:Duration2-1                            3.127e-01  1.355e-01  4.016e+01   2.309   0.0262 *  
## Category2-1:Duration2-1                        1.223e-01  9.173e-02  4.585e+01   1.333   0.1891    
## Type2-1:Hemisphere2-1                          1.154e-02  6.999e-02  7.068e+04   0.165   0.8691    
## Category2-1:Hemisphere2-1                     -4.049e-02  6.999e-02  7.068e+04  -0.579   0.5629    
## Duration2-1:Hemisphere2-1                      5.761e-03  1.154e-01  3.009e+01   0.050   0.9605    
## Type2-1:Category2-1:Duration2-1                7.014e-03  1.791e-01  9.725e+01   0.039   0.9688    
## Type2-1:Category2-1:Hemisphere2-1              1.898e-01  1.400e-01  7.068e+04   1.356   0.1752    
## Type2-1:Duration2-1:Hemisphere2-1             -4.251e-02  1.400e-01  7.068e+04  -0.304   0.7613    
## Category2-1:Duration2-1:Hemisphere2-1         -1.505e-01  1.400e-01  7.068e+04  -1.075   0.2822    
## Type2-1:Category2-1:Duration2-1:Hemisphere2-1  1.214e-01  2.800e-01  7.068e+04   0.434   0.6646    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular

5.2.2.3 The reduced model

summary(rePCA(lmm.zcp.P1.E2))

## $Stimuli
## Importance of components:
##                          [,1]    [,2]    [,3]    [,4]      [,5]      [,6]      [,7] [,8]
## Standard deviation     0.1261 0.05655 0.04809 0.03062 1.109e-05 1.704e-07 1.308e-07    0
## Proportion of Variance 0.7114 0.14312 0.10350 0.04196 0.000e+00 0.000e+00 0.000e+00    0
## Cumulative Proportion  0.7114 0.85454 0.95804 1.00000 1.000e+00 1.000e+00 1.000e+00    1
## 
## $SubjCode
## Importance of components:
##                          [,1]   [,2]    [,3]    [,4]    [,5]    [,6]    [,7]    [,8]    [,9]     [,10]     [,11] [,12] [,13] [,14] [,15] [,16]
## Standard deviation     0.3368 0.2021 0.14111 0.14047 0.13262 0.12681 0.08195 0.05108 0.04373 4.734e-07 1.105e-07     0     0     0     0     0
## Proportion of Variance 0.4749 0.1710 0.08339 0.08264 0.07366 0.06734 0.02813 0.01093 0.00801 0.000e+00 0.000e+00     0     0     0     0     0
## Cumulative Proportion  0.4749 0.6459 0.72930 0.81194 0.88559 0.95294 0.98106 0.99199 1.00000 1.000e+00 1.000e+00     1     1     1     1     1

file.rdc.P1.E2 <- file.path(folder_nesi, "E205_P1_lmm_rdc.RData")

# fit the rdc model
if (!file.exists(file.rdc.P1.E2)) {
  # lmm.rdc.P1.E2.step <- step(lmm.rdc.P1.E2, reduce.fixed = FALSE)
  
  lmm.rdc.P1.E2 <- update(lmm.zcp.P1.E2,
                          formula = MeanAmp ~ Type * Category * Duration * Hemisphere + 
                            (1 + Type_C + Cate_C + Dura_C + Hemi_C +  
                               Type_Cate + Type_Dura + Cate_Dura + Dura_Hemi
                             || SubjCode) +
                            (1 + Type_C + Dura_C +
                               Type_Dura 
                             || Stimuli))
  
} else {
  load(file.rdc.P1.E2)
}

print(summary(lmm.rdc.P1.E2), corr = FALSE)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Type + Category + Duration + Hemisphere + (1 + Type_C +      Cate_C + Dura_C + Hemi_C + Type_Cate + Type_Dura + Cate_Dura +      Dura_Hemi || SubjCode) + (1 + Type_C + Dura_C + Type_Dura ||      Stimuli) + Type:Category + Type:Duration + Category:Duration +      Type:Hemisphere + Category:Hemisphere + Duration:Hemisphere +      Type:Category:Duration + Type:Category:Hemisphere + Type:Duration:Hemisphere +      Category:Duration:Hemisphere + Type:Category:Duration:Hemisphere
##    Data: df.erp.P1.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+07))
## 
## REML criterion at convergence: 399290.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.5143 -0.5726 -0.0123  0.5716  9.3014 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev.
##  Stimuli    Type_Dura    0.24958 0.4996  
##  Stimuli.1  Dura_C       0.05021 0.2241  
##  Stimuli.2  Type_C       0.03631 0.1906  
##  Stimuli.3  (Intercept)  0.01472 0.1213  
##  SubjCode   Dura_Hemi    0.25250 0.5025  
##  SubjCode.1 Cate_Dura    0.03003 0.1733  
##  SubjCode.2 Type_Dura    0.30984 0.5566  
##  SubjCode.3 Type_Cate    0.27617 0.5255  
##  SubjCode.4 Hemi_C       0.64112 0.8007  
##  SubjCode.5 Dura_C       0.31265 0.5592  
##  SubjCode.6 Cate_C       0.04097 0.2024  
##  SubjCode.7 Type_C       0.10546 0.3248  
##  SubjCode.8 (Intercept)  1.78060 1.3344  
##  Residual               15.70241 3.9626  
## Number of obs: 71282, groups:  Stimuli, 80; SubjCode, 30
## 
## Fixed effects:
##                                                 Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)                                    1.263e+00  2.446e-01  2.918e+01   5.162 1.59e-05 ***
## Type2-1                                       -1.068e-01  7.208e-02  3.472e+01  -1.482   0.1474    
## Category2-1                                    8.635e-02  5.768e-02  4.827e+01   1.497   0.1409    
## Duration2-1                                    3.532e-01  1.108e-01  3.198e+01   3.187   0.0032 ** 
## Hemisphere2-1                                  1.172e-01  1.503e-01  2.930e+01   0.780   0.4419    
## Type2-1:Category2-1                            1.227e-01  1.262e-01  4.055e+01   0.972   0.3366    
## Type2-1:Duration2-1                            3.127e-01  1.355e-01  4.016e+01   2.309   0.0262 *  
## Category2-1:Duration2-1                        1.223e-01  9.173e-02  4.585e+01   1.333   0.1891    
## Type2-1:Hemisphere2-1                          1.154e-02  6.999e-02  7.068e+04   0.165   0.8691    
## Category2-1:Hemisphere2-1                     -4.049e-02  6.999e-02  7.068e+04  -0.579   0.5629    
## Duration2-1:Hemisphere2-1                      5.761e-03  1.154e-01  3.009e+01   0.050   0.9605    
## Type2-1:Category2-1:Duration2-1                7.014e-03  1.791e-01  9.725e+01   0.039   0.9688    
## Type2-1:Category2-1:Hemisphere2-1              1.898e-01  1.400e-01  7.068e+04   1.356   0.1752    
## Type2-1:Duration2-1:Hemisphere2-1             -4.251e-02  1.400e-01  7.068e+04  -0.304   0.7613    
## Category2-1:Duration2-1:Hemisphere2-1         -1.505e-01  1.400e-01  7.068e+04  -1.075   0.2822    
## Type2-1:Category2-1:Duration2-1:Hemisphere2-1  1.214e-01  2.800e-01  7.068e+04   0.434   0.6646    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

anova(lmm.rdc.P1.E2, lmm.zcp.P1.E2, refit = FALSE)

## Data: df.erp.P1.E2
## Models:
## lmm.rdc.P1.E2: MeanAmp ~ Type + Category + Duration + Hemisphere + (1 + Type_C + 
## lmm.rdc.P1.E2:     Cate_C + Dura_C + Hemi_C + Type_Cate + Type_Dura + Cate_Dura + 
## lmm.rdc.P1.E2:     Dura_Hemi || SubjCode) + (1 + Type_C + Dura_C + Type_Dura || 
## lmm.rdc.P1.E2:     Stimuli) + Type:Category + Type:Duration + Category:Duration + 
## lmm.rdc.P1.E2:     Type:Hemisphere + Category:Hemisphere + Duration:Hemisphere + 
## lmm.rdc.P1.E2:     Type:Category:Duration + Type:Category:Hemisphere + Type:Duration:Hemisphere + 
## lmm.rdc.P1.E2:     Category:Duration:Hemisphere + Type:Category:Duration:Hemisphere
## lmm.zcp.P1.E2: MeanAmp ~ Type * Category * Duration * Hemisphere + (1 + Type_C + 
## lmm.zcp.P1.E2:     Cate_C + Dura_C + Hemi_C + Type_Cate + Type_Dura + Cate_Dura + 
## lmm.zcp.P1.E2:     Type_Hemi + Cate_Hemi + Dura_Hemi + Type_Cate_Dura + Type_Cate_Hemi + 
## lmm.zcp.P1.E2:     Type_Dura_Hemi + Cate_Dura_Hemi + Type_Cate_Dura_Hemi || 
## lmm.zcp.P1.E2:     SubjCode) + (1 + Type_C + Dura_C + Hemi_C + Type_Dura + Type_Hemi + 
## lmm.zcp.P1.E2:     Dura_Hemi + Type_Dura_Hemi || Stimuli)
##               npar    AIC    BIC  logLik deviance Chisq Df Pr(>Chisq)
## lmm.rdc.P1.E2   30 399351 399626 -199645   399291                    
## lmm.zcp.P1.E2   41 399373 399749 -199645   399291     0 11          1

5.2.2.4 The extended model

file.etd.P1.E2 <- file.path(folder_nesi, "E205_P1_lmm_etd.RData")

# fit the etd model
if (!file.exists(file.etd.P1.E2)) {
  
  lmm.etd.P1.E2 <- update(lmm.rdc.P1.E2,
                          formula = MeanAmp ~ Type * Category * Duration * Hemisphere + 
                            (1 + Type_C + Cate_C + Dura_C + Hemi_C +  
                               Type_Cate + Type_Dura + Cate_Dura + Dura_Hemi
                             | SubjCode) +
                            (1 + Type_C + Dura_C + 
                               Type_Dura
                             | Stimuli))
  
  lmm.etd.P1.E2.2 <- re_fit(lmm.etd.P1.E2)
  
} else {
  load(file.etd.P1.E2)
}

print(summary(lmm.etd.P1.E2.2), corr = FALSE)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Type + Category + Duration + Hemisphere + (1 + Type_C +      Cate_C + Dura_C + Hemi_C + Type_Cate + Type_Dura + Cate_Dura +      Dura_Hemi | SubjCode) + (1 + Type_C + Dura_C + Type_Dura |      Stimuli) + Type:Category + Type:Duration + Category:Duration +      Type:Hemisphere + Category:Hemisphere + Duration:Hemisphere +      Type:Category:Duration + Type:Category:Hemisphere + Type:Duration:Hemisphere +      Category:Duration:Hemisphere + Type:Category:Duration:Hemisphere
##    Data: df.erp.P1.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+07))
## 
## REML criterion at convergence: 399219.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.4381 -0.5727 -0.0121  0.5713  9.2907 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr                                           
##  Stimuli  (Intercept)  0.02013 0.1419                                                  
##           Type_C       0.06908 0.2628    0.06                                          
##           Dura_C       0.07107 0.2666    0.40 -0.06                                    
##           Type_Dura    0.41428 0.6436   -0.10  0.67  0.12                              
##  SubjCode (Intercept)  1.79762 1.3408                                                  
##           Type_C       0.11245 0.3353    0.11                                          
##           Cate_C       0.04079 0.2020   -0.35 -0.31                                    
##           Dura_C       0.32097 0.5665    0.44 -0.09 -0.37                              
##           Hemi_C       0.61141 0.7819    0.30  0.11  0.50 -0.19                        
##           Type_Cate    0.25555 0.5055    0.62  0.23 -0.23  0.31  0.22                  
##           Type_Dura    0.33290 0.5770    0.05  0.22  0.07  0.29  0.03  0.43            
##           Cate_Dura    0.04222 0.2055    0.45 -0.45  0.13  0.10  0.58  0.41 -0.23      
##           Dura_Hemi    0.23709 0.4869    0.22 -0.08 -0.60  0.48 -0.43  0.20  0.16  0.14
##  Residual             15.69460 3.9616                                                  
## Number of obs: 71282, groups:  Stimuli, 80; SubjCode, 30
## 
## Fixed effects:
##                                                 Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)                                    1.263e+00  2.459e-01  2.924e+01   5.135  1.7e-05 ***
## Type2-1                                       -1.065e-01  7.640e-02  3.752e+01  -1.394  0.17163    
## Category2-1                                    8.568e-02  5.993e-02  4.572e+01   1.430  0.15958    
## Duration2-1                                    3.540e-01  1.132e-01  3.306e+01   3.127  0.00367 ** 
## Hemisphere2-1                                  1.175e-01  1.470e-01  2.908e+01   0.799  0.43066    
## Type2-1:Category2-1                            1.216e-01  1.299e-01  4.603e+01   0.936  0.35391    
## Type2-1:Duration2-1                            3.135e-01  1.455e-01  4.433e+01   2.154  0.03669 *  
## Category2-1:Duration2-1                        1.222e-01  9.930e-02  6.054e+01   1.231  0.22312    
## Type2-1:Hemisphere2-1                          1.162e-02  6.997e-02  7.073e+04   0.166  0.86805    
## Category2-1:Hemisphere2-1                     -4.073e-02  6.997e-02  7.073e+04  -0.582  0.56048    
## Duration2-1:Hemisphere2-1                      6.368e-03  1.131e-01  2.998e+01   0.056  0.95549    
## Type2-1:Category2-1:Duration2-1                6.142e-03  2.008e-01  7.769e+01   0.031  0.97567    
## Type2-1:Category2-1:Hemisphere2-1              1.886e-01  1.399e-01  7.073e+04   1.348  0.17771    
## Type2-1:Duration2-1:Hemisphere2-1             -4.223e-02  1.399e-01  7.073e+04  -0.302  0.76282    
## Category2-1:Duration2-1:Hemisphere2-1         -1.508e-01  1.399e-01  7.073e+04  -1.078  0.28117    
## Type2-1:Category2-1:Duration2-1:Hemisphere2-1  1.195e-01  2.799e-01  7.073e+04   0.427  0.66937    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

anova(lmm.etd.P1.E2.2, lmm.rdc.P1.E2, refit = FALSE)

## Data: df.erp.P1.E2
## Models:
## lmm.rdc.P1.E2: MeanAmp ~ Type + Category + Duration + Hemisphere + (1 + Type_C + 
## lmm.rdc.P1.E2:     Cate_C + Dura_C + Hemi_C + Type_Cate + Type_Dura + Cate_Dura + 
## lmm.rdc.P1.E2:     Dura_Hemi || SubjCode) + (1 + Type_C + Dura_C + Type_Dura || 
## lmm.rdc.P1.E2:     Stimuli) + Type:Category + Type:Duration + Category:Duration + 
## lmm.rdc.P1.E2:     Type:Hemisphere + Category:Hemisphere + Duration:Hemisphere + 
## lmm.rdc.P1.E2:     Type:Category:Duration + Type:Category:Hemisphere + Type:Duration:Hemisphere + 
## lmm.rdc.P1.E2:     Category:Duration:Hemisphere + Type:Category:Duration:Hemisphere
## lmm.etd.P1.E2.2: MeanAmp ~ Type + Category + Duration + Hemisphere + (1 + Type_C + 
## lmm.etd.P1.E2.2:     Cate_C + Dura_C + Hemi_C + Type_Cate + Type_Dura + Cate_Dura + 
## lmm.etd.P1.E2.2:     Dura_Hemi | SubjCode) + (1 + Type_C + Dura_C + Type_Dura | 
## lmm.etd.P1.E2.2:     Stimuli) + Type:Category + Type:Duration + Category:Duration + 
## lmm.etd.P1.E2.2:     Type:Hemisphere + Category:Hemisphere + Duration:Hemisphere + 
## lmm.etd.P1.E2.2:     Type:Category:Duration + Type:Category:Hemisphere + Type:Duration:Hemisphere + 
## lmm.etd.P1.E2.2:     Category:Duration:Hemisphere + Type:Category:Duration:Hemisphere
##                 npar    AIC    BIC  logLik deviance  Chisq Df Pr(>Chisq)   
## lmm.rdc.P1.E2     30 399351 399626 -199645   399291                        
## lmm.etd.P1.E2.2   72 399364 400024 -199610   399220 70.985 42   0.003421 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

The reduced model (lmm.rdc.P1.E2) explained the data better than the extended model (lmm.rdc.P1.E2.2).

5.2.2.5 The optimal model

lmm.opt.P1.E205 <- lmm.rdc.P1.E2

# print(summary(lmm.opt.P1.E2), corr = FALSE)

summary(lmm.opt.P1.E205)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Type + Category + Duration + Hemisphere + (1 + Type_C +      Cate_C + Dura_C + Hemi_C + Type_Cate + Type_Dura + Cate_Dura +      Dura_Hemi || SubjCode) + (1 + Type_C + Dura_C + Type_Dura ||      Stimuli) + Type:Category + Type:Duration + Category:Duration +      Type:Hemisphere + Category:Hemisphere + Duration:Hemisphere +      Type:Category:Duration + Type:Category:Hemisphere + Type:Duration:Hemisphere +      Category:Duration:Hemisphere + Type:Category:Duration:Hemisphere
##    Data: df.erp.P1.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+07))
## 
## REML criterion at convergence: 399290.8
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.5143 -0.5726 -0.0123  0.5716  9.3014 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev.
##  Stimuli    Type_Dura    0.24958 0.4996  
##  Stimuli.1  Dura_C       0.05021 0.2241  
##  Stimuli.2  Type_C       0.03631 0.1906  
##  Stimuli.3  (Intercept)  0.01472 0.1213  
##  SubjCode   Dura_Hemi    0.25250 0.5025  
##  SubjCode.1 Cate_Dura    0.03003 0.1733  
##  SubjCode.2 Type_Dura    0.30984 0.5566  
##  SubjCode.3 Type_Cate    0.27617 0.5255  
##  SubjCode.4 Hemi_C       0.64112 0.8007  
##  SubjCode.5 Dura_C       0.31265 0.5592  
##  SubjCode.6 Cate_C       0.04097 0.2024  
##  SubjCode.7 Type_C       0.10546 0.3248  
##  SubjCode.8 (Intercept)  1.78060 1.3344  
##  Residual               15.70241 3.9626  
## Number of obs: 71282, groups:  Stimuli, 80; SubjCode, 30
## 
## Fixed effects:
##                                                 Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)                                    1.263e+00  2.446e-01  2.918e+01   5.162 1.59e-05 ***
## Type2-1                                       -1.068e-01  7.208e-02  3.472e+01  -1.482   0.1474    
## Category2-1                                    8.635e-02  5.768e-02  4.827e+01   1.497   0.1409    
## Duration2-1                                    3.532e-01  1.108e-01  3.198e+01   3.187   0.0032 ** 
## Hemisphere2-1                                  1.172e-01  1.503e-01  2.930e+01   0.780   0.4419    
## Type2-1:Category2-1                            1.227e-01  1.262e-01  4.055e+01   0.972   0.3366    
## Type2-1:Duration2-1                            3.127e-01  1.355e-01  4.016e+01   2.309   0.0262 *  
## Category2-1:Duration2-1                        1.223e-01  9.173e-02  4.585e+01   1.333   0.1891    
## Type2-1:Hemisphere2-1                          1.154e-02  6.999e-02  7.068e+04   0.165   0.8691    
## Category2-1:Hemisphere2-1                     -4.049e-02  6.999e-02  7.068e+04  -0.579   0.5629    
## Duration2-1:Hemisphere2-1                      5.761e-03  1.154e-01  3.009e+01   0.050   0.9605    
## Type2-1:Category2-1:Duration2-1                7.014e-03  1.791e-01  9.725e+01   0.039   0.9688    
## Type2-1:Category2-1:Hemisphere2-1              1.898e-01  1.400e-01  7.068e+04   1.356   0.1752    
## Type2-1:Duration2-1:Hemisphere2-1             -4.251e-02  1.400e-01  7.068e+04  -0.304   0.7613    
## Category2-1:Duration2-1:Hemisphere2-1         -1.505e-01  1.400e-01  7.068e+04  -1.075   0.2822    
## Type2-1:Category2-1:Duration2-1:Hemisphere2-1  1.214e-01  2.800e-01  7.068e+04   0.434   0.6646    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# confint(lmm.opt.P1.E205, method="Wald")

5.2.2.6 Diagnostic plots

# qqplot
qqplot_lmer(lmm.opt.P1.E205)

5.2.2.7 Estimated marginal means

emm.P1.E205 <- emmeans(lmm.opt.P1.E205, ~ Type + Hemisphere + Category + Duration)

emm.P1.E205 %>% 
  as.data.frame() %>% 
  arrange(Type, Hemisphere, Category, Duration)

##         Type Hemisphere Category Duration    emmean        SE       df  lower.CL upper.CL
## 1     intact       Left     face       17 1.1785874 0.2708918 43.29814 0.6323907 1.724784
## 2     intact       Left     face      200 1.2496632 0.2805657 49.82228 0.6860806 1.813246
## 3     intact       Left    house       17 1.1590794 0.2708794 43.29021 0.6129048 1.705254
## 4     intact       Left    house      200 1.4545431 0.2807221 49.93343 0.8906776 2.018409
## 5     intact      Right     face       17 1.2913750 0.2708918 43.29814 0.7451783 1.837572
## 6     intact      Right     face      200 1.4950817 0.2805657 49.82228 0.9314991 2.058664
## 7     intact      Right    house       17 1.2420974 0.2708794 43.29021 0.6959229 1.788272
## 8     intact      Right    house      200 1.4589674 0.2807221 49.93343 0.8951019 2.022833
## 9  scrambled       Left     face       17 0.8716930 0.2741473 45.41736 0.3196719 1.423714
## 10 scrambled       Left     face      200 1.3036114 0.2807754 49.97141 0.7396493 1.867573
## 11 scrambled       Left    house       17 0.9068404 0.2741488 45.41832 0.3548168 1.458864
## 12 scrambled       Left    house      200 1.5094707 0.2807453 49.94998 0.9455631 2.073378
## 13 scrambled      Right     face       17 0.9527330 0.2741473 45.41736 0.4007120 1.504754
## 14 scrambled      Right     face      200 1.4140765 0.2807754 49.97141 0.8501144 1.978039
## 15 scrambled      Right    house       17 1.0871965 0.2741488 45.41832 0.5351728 1.639220
## 16 scrambled      Right    house      200 1.6294081 0.2807453 49.94998 1.0655005 2.193316

5.2.2.8 Plots

# emmip(emm.P1.E205, Category ~ Duration | Type + Hemisphere, CIs = TRUE)

hemiLabel <- c("left", "right")
names(hemiLabel) <- c("Left", "Right")

p1.all.LinePlot = {
  ggplot(data = data.frame(emm.P1.E205), aes(y = emmean, x = Duration, color = Category, group = Category)) +  # set the data, varialbes for x and y axises, and the variable for dividing data
    geom_point(size = 2) +
    geom_line(aes(linetype = Category), size = .85) +  # position = "dodge", alpha = .7
    scale_linetype_manual(values=c("solid", "dashed")) +
    geom_errorbar(mapping = aes(ymin = lower.CL, ymax = upper.CL), linetype = 1,  # set the error bar
                  show.legend = FALSE, width = 0.25, alpha = .5) + # ,                   position = position_dodge(width=0.9)
    facet_grid(Type ~ Hemisphere, # switch = "x", 
               labeller = labeller(Hemisphere = hemiLabel)) +
    scale_y_reverse(limits = ylimit_p1) +  # set the limit for y axis
    # scale_y_continuous(expand= c(0, 0)) +  # remove the space between columns and x axis
    labs(x = "Duration (ms)", y = expression(paste("P1 Amplitudes (", mu, "V)")), fill = "Category") +  # set the names for main, x and y axises title = "", 
    # scale_x_discrete(labels = c("left", "right")) +
    # geom_text(label = c("", "", "", "", "***", "", "", ""), color = "red", size = 6, nudge_y = 0.5, nudge_x = 0.5) + # add starts to the significant columns
    theme_bw() +
    erp_theme 
}

# p1.all.LinePlot.slide <- {
#   p1.all.LinePlot + 
#     theme(legend.position="right",
#           axis.line = element_line(color="black", size = 3))
# }
# ggsave('P1_all.png', p1.all.LinePlot.slide, width = 7, height = 4)

p1.all.LinePlot

library(RColorBrewer)
duration_color <- c(brewer.pal(9, 'Blues')[c(6:8)], brewer.pal(12, "Paired")[6]) 
category_color <- brewer.pal(8, 'Dark2')[c(6,1)]

p1_all_LinePlot <- {
  ggplot(data = data.frame(emm.P1.E205), aes(y = emmean, x = Duration, group = Category)) +  # set the data, varialbes for x and y axises, and the variable for dividing data
    geom_point(aes(fill = Duration), size=4, shape=21, stroke=0) +
    scale_fill_manual(values = duration_color[c(3, 4)], guide = FALSE) +
    geom_line(aes(linetype = Category, color = Category),  size = .85) +  # position = "dodge", alpha = .7
    scale_color_manual(values=category_color, limits = c("face", "house")) +
    scale_linetype_manual(values=c("solid", "dashed")) +
    geom_errorbar(mapping = aes(ymin = lower.CL, ymax = upper.CL), linetype = 1,  # set the error bar
                  show.legend = FALSE, width = 0.25, alpha = .5) + # ,                   position = position_dodge(width=0.9)
    facet_grid(Type ~ Hemisphere, # switch = "x", 
               labeller = labeller(Hemisphere = hemiLabel)) +
    scale_y_reverse(limits = ylimit_p1) +  # set the limit for y axis
    # scale_y_continuous(expand= c(0, 0)) +  # remove the space between columns and x axis
    labs(x = "Duration (ms)", y = expression(paste("P1 Amplitudes (", mu, "V)")), color = "Category", linetype = "Category") +  # set the names for main, x and y axises title = "", 
    # scale_x_discrete(labels = c("left", "right")) +
    # geom_text(label = c("", "", "", "", "***", "", "", ""), color = "red", size = 6, nudge_y = 0.5, nudge_x = 0.5) + # add starts to the significant columns
    theme_bw() +
    erp_theme +
    theme(
      legend.position = c(.5, .5),
      legend.title=element_text(size=10), 
      legend.text=element_text(size=10),
    ) +
    guides(linetype = guide_legend(title.position = "left", nrow = 1),
           color = guide_legend(title.position = "left", nrow = 1))
}

ggsave('P1_all_pub.pdf', p1_all_LinePlot, width = 6, height = 5)

5.2.2.9 Contrasts

5.2.2.9.1 Main results for intact stimuli

Comparisons of P1 amplitudes for intact faces with different durations:

contra_all_P1 <- contrast(emmeans(lmm.opt.P1.E205, ~ Duration | Hemisphere + Category + Type), 
                              "pairwise", combine = TRUE, adjust = "Bonferroni", infer = TRUE)

contra_all_P1[1:2]

##  contrast Hemisphere Category Type      estimate        SE     df   lower.CL  upper.CL t.ratio p.value
##  17 - 200 Left       face     intact -0.07107583 0.1643558 122.97 -0.4440288 0.3018771  -0.432  1.0000
##  17 - 200 Right      face     intact -0.20370672 0.1643558 122.97 -0.5766597 0.1692462  -1.239  0.4351
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 2 estimates 
## P value adjustment: bonferroni method for 2 tests

summary(contra_all_P1[1:2], delta = delta_null, level = .9)

##  contrast Hemisphere Category Type      estimate        SE     df   lower.CL  upper.CL t.ratio p.value
##  17 - 200 Left       face     intact -0.07107583 0.1643558 122.97 -0.3964090 0.2542574  -2.610  0.0102
##  17 - 200 Right      face     intact -0.20370672 0.1643558 122.97 -0.5290399 0.1216265  -1.803  0.0739
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Conf-level adjustment: bonferroni method for 2 estimates 
## P value adjustment: bonferroni method for 2 tests 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

summary(contra_all_P1[1:2], delta = delta_null, side = ">")

##  contrast Hemisphere Category Type      estimate        SE     df   lower.CL upper.CL t.ratio p.value
##  17 - 200 Left       face     intact -0.07107583 0.1643558 122.97 -0.3964090      Inf   2.610  0.0102
##  17 - 200 Right      face     intact -0.20370672 0.1643558 122.97 -0.5290399      Inf   1.803  0.0739
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 2 estimates 
## P value adjustment: bonferroni method for 2 tests 
## Statistics are tests of noninferiority with a threshold of 0.5 
## P values are right-tailed

summary(contra_all_P1[1:2], delta = delta_null, side = "<")

##  contrast Hemisphere Category Type      estimate        SE     df lower.CL  upper.CL t.ratio p.value
##  17 - 200 Left       face     intact -0.07107583 0.1643558 122.97     -Inf 0.2542574  -3.475  0.0007
##  17 - 200 Right      face     intact -0.20370672 0.1643558 122.97     -Inf 0.1216265  -4.282  <.0001
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 2 estimates 
## P value adjustment: bonferroni method for 2 tests 
## Statistics are tests of nonsuperiority with a threshold of 0.5 
## P values are left-tailed

Comparisons of face-specific P1 amplitude components for intact stimuli with different durations:

contra_all_P1_spec <- contrast(emmeans(lmm.opt.P1.E205, ~ Category + Duration | Hemisphere + Type), 
                               interaction = "pairwise", adjust = "Bonferroni", infer = TRUE)
contra_all_P1_spec[1:2]

##  Category_pairwise Duration_pairwise Hemisphere Type     estimate        SE     df   lower.CL  upper.CL t.ratio p.value
##  face - house      17 - 200          Left       intact 0.22438787 0.1561692 266.23 -0.1276411 0.5764168   1.437  0.3039
##  face - house      17 - 200          Right      intact 0.01316323 0.1561692 266.23 -0.3388657 0.3651922   0.084  1.0000
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 2 estimates 
## P value adjustment: bonferroni method for 2 tests

summary(contra_all_P1_spec[1:2], delta = delta_null, level = .9)

##  Category_pairwise Duration_pairwise Hemisphere Type     estimate        SE     df    lower.CL  upper.CL t.ratio p.value
##  face - house      17 - 200          Left       intact 0.22438787 0.1561692 266.23 -0.08309597 0.5318717  -1.765  0.0787
##  face - house      17 - 200          Right      intact 0.01316323 0.1561692 266.23 -0.29432060 0.3206471  -3.117  0.0020
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Conf-level adjustment: bonferroni method for 2 estimates 
## P value adjustment: bonferroni method for 2 tests 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

summary(contra_all_P1_spec[1:2], delta = delta_null, side = ">")

##  Category_pairwise Duration_pairwise Hemisphere Type     estimate        SE     df    lower.CL upper.CL t.ratio p.value
##  face - house      17 - 200          Left       intact 0.22438787 0.1561692 266.23 -0.08309597      Inf   4.638  <.0001
##  face - house      17 - 200          Right      intact 0.01316323 0.1561692 266.23 -0.29432060      Inf   3.286  0.0012
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 2 estimates 
## P value adjustment: bonferroni method for 2 tests 
## Statistics are tests of noninferiority with a threshold of 0.5 
## P values are right-tailed

summary(contra_all_P1_spec[1:2], delta = delta_null, side = "<")

##  Category_pairwise Duration_pairwise Hemisphere Type     estimate        SE     df lower.CL  upper.CL t.ratio p.value
##  face - house      17 - 200          Left       intact 0.22438787 0.1561692 266.23     -Inf 0.5318717  -1.765  0.0787
##  face - house      17 - 200          Right      intact 0.01316323 0.1561692 266.23     -Inf 0.3206471  -3.117  0.0020
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 2 estimates 
## P value adjustment: bonferroni method for 2 tests 
## Statistics are tests of nonsuperiority with a threshold of 0.5 
## P values are left-tailed

5.2.2.9.2 Main results for scrambled stimuli

Comparisons of P1 amplitudes for scrambled faces with different durations:

contra_all_P1[5:6]

##  contrast Hemisphere Category Type        estimate        SE     df   lower.CL    upper.CL t.ratio p.value
##  17 - 200 Left       face     scrambled -0.4319184 0.1700147 140.79 -0.8171081 -0.04672872  -2.540  0.0243
##  17 - 200 Right      face     scrambled -0.4613435 0.1700147 140.79 -0.8465331 -0.07615380  -2.714  0.0150
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 2 estimates 
## P value adjustment: bonferroni method for 2 tests

summary(contra_all_P1[5:6], delta = delta_null, level = .9)

##  contrast Hemisphere Category Type        estimate        SE     df   lower.CL   upper.CL t.ratio p.value
##  17 - 200 Left       face     scrambled -0.4319184 0.1700147 140.79 -0.7680300 -0.0958068  -0.400  0.6894
##  17 - 200 Right      face     scrambled -0.4613435 0.1700147 140.79 -0.7974551 -0.1252319  -0.227  0.8205
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Conf-level adjustment: bonferroni method for 2 estimates 
## P value adjustment: bonferroni method for 2 tests 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

summary(contra_all_P1[5:6], delta = delta_null, side = ">")

##  contrast Hemisphere Category Type        estimate        SE     df   lower.CL upper.CL t.ratio p.value
##  17 - 200 Left       face     scrambled -0.4319184 0.1700147 140.79 -0.7680300      Inf   0.400  0.6894
##  17 - 200 Right      face     scrambled -0.4613435 0.1700147 140.79 -0.7974551      Inf   0.227  0.8205
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 2 estimates 
## P value adjustment: bonferroni method for 2 tests 
## Statistics are tests of noninferiority with a threshold of 0.5 
## P values are right-tailed

summary(contra_all_P1[5:6], delta = delta_null, side = "<")

##  contrast Hemisphere Category Type        estimate        SE     df lower.CL   upper.CL t.ratio p.value
##  17 - 200 Left       face     scrambled -0.4319184 0.1700147 140.79     -Inf -0.0958068  -5.481  <.0001
##  17 - 200 Right      face     scrambled -0.4613435 0.1700147 140.79     -Inf -0.1252319  -5.654  <.0001
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 2 estimates 
## P value adjustment: bonferroni method for 2 tests 
## Statistics are tests of nonsuperiority with a threshold of 0.5 
## P values are left-tailed

Comparisons of face-specific P1 amplitude components for scrambled stimuli with different durations:

contra_all_P1_spec[3:4]

##  Category_pairwise Duration_pairwise Hemisphere Type        estimate        SE     df   lower.CL  upper.CL t.ratio p.value
##  face - house      17 - 200          Left       scrambled 0.17071189 0.1675616 352.74 -0.2064712 0.5478950   1.019  0.6180
##  face - house      17 - 200          Right      scrambled 0.08086813 0.1675616 352.74 -0.2963150 0.4580513   0.483  1.0000
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 2 estimates 
## P value adjustment: bonferroni method for 2 tests

summary(contra_all_P1_spec[3:4], delta = delta_null, level = .9)

##  Category_pairwise Duration_pairwise Hemisphere Type        estimate        SE     df   lower.CL  upper.CL t.ratio p.value
##  face - house      17 - 200          Left       scrambled 0.17071189 0.1675616 352.74 -0.1588335 0.5002573  -1.965  0.0502
##  face - house      17 - 200          Right      scrambled 0.08086813 0.1675616 352.74 -0.2486773 0.4104136  -2.501  0.0128
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Conf-level adjustment: bonferroni method for 2 estimates 
## P value adjustment: bonferroni method for 2 tests 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

summary(contra_all_P1_spec[3:4], delta = delta_null, side = ">")

##  Category_pairwise Duration_pairwise Hemisphere Type        estimate        SE     df   lower.CL upper.CL t.ratio p.value
##  face - house      17 - 200          Left       scrambled 0.17071189 0.1675616 352.74 -0.1588335      Inf   4.003  0.0001
##  face - house      17 - 200          Right      scrambled 0.08086813 0.1675616 352.74 -0.2486773      Inf   3.467  0.0006
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 2 estimates 
## P value adjustment: bonferroni method for 2 tests 
## Statistics are tests of noninferiority with a threshold of 0.5 
## P values are right-tailed

summary(contra_all_P1_spec[3:4], delta = delta_null, side = "<")

##  Category_pairwise Duration_pairwise Hemisphere Type        estimate        SE     df lower.CL  upper.CL t.ratio p.value
##  face - house      17 - 200          Left       scrambled 0.17071189 0.1675616 352.74     -Inf 0.5002573  -1.965  0.0502
##  face - house      17 - 200          Right      scrambled 0.08086813 0.1675616 352.74     -Inf 0.4104136  -2.501  0.0128
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 2 estimates 
## P value adjustment: bonferroni method for 2 tests 
## Statistics are tests of nonsuperiority with a threshold of 0.5 
## P values are left-tailed

5.2.2.9.3 Other results

Comparisons of P1 amplitudes between Durations (without multiple comparison corrections):

summary(contra_all_P1[1:8], adjust = "none")

##  contrast Hemisphere Category Type        estimate        SE     df   lower.CL    upper.CL t.ratio p.value
##  17 - 200 Left       face     intact    -0.0710758 0.1643558 122.97 -0.3964090  0.25425736  -0.432  0.6662
##  17 - 200 Right      face     intact    -0.2037067 0.1643558 122.97 -0.5290399  0.12162647  -1.239  0.2175
##  17 - 200 Left       house    intact    -0.2954637 0.1646024 123.70 -0.6212656  0.03033818  -1.795  0.0751
##  17 - 200 Right      house    intact    -0.2168700 0.1646024 123.70 -0.5426718  0.10893192  -1.318  0.1901
##  17 - 200 Left       face     scrambled -0.4319184 0.1700147 140.79 -0.7680300 -0.09580680  -2.540  0.0122
##  17 - 200 Right      face     scrambled -0.4613435 0.1700147 140.79 -0.7974551 -0.12523188  -2.714  0.0075
##  17 - 200 Left       house    scrambled -0.6026303 0.1699668 140.64 -0.9386505 -0.26661003  -3.546  0.0005
##  17 - 200 Right      house    scrambled -0.5422116 0.1699668 140.64 -0.8782319 -0.20619135  -3.190  0.0018
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95

summary(contra_all_P1[1:8], delta = delta_null, side = "two.sided", adjust = "none")

##  contrast Hemisphere Category Type        estimate        SE     df   lower.CL    upper.CL t.ratio p.value
##  17 - 200 Left       face     intact    -0.0710758 0.1643558 122.97 -0.3964090  0.25425736  -2.610  0.0051
##  17 - 200 Right      face     intact    -0.2037067 0.1643558 122.97 -0.5290399  0.12162647  -1.803  0.0369
##  17 - 200 Left       house    intact    -0.2954637 0.1646024 123.70 -0.6212656  0.03033818  -1.243  0.1082
##  17 - 200 Right      house    intact    -0.2168700 0.1646024 123.70 -0.5426718  0.10893192  -1.720  0.0440
##  17 - 200 Left       face     scrambled -0.4319184 0.1700147 140.79 -0.7680300 -0.09580680  -0.400  0.3447
##  17 - 200 Right      face     scrambled -0.4613435 0.1700147 140.79 -0.7974551 -0.12523188  -0.227  0.4102
##  17 - 200 Left       house    scrambled -0.6026303 0.1699668 140.64 -0.9386505 -0.26661003   0.604  0.7265
##  17 - 200 Right      house    scrambled -0.5422116 0.1699668 140.64 -0.8782319 -0.20619135   0.248  0.5979
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

Comparisons of face-specific P1 amplitude components between Durations (without multiple comparison corrections):

summary(contra_all_P1_spec[1:4], adjust = "none")

##  Category_pairwise Duration_pairwise Hemisphere Type        estimate        SE     df    lower.CL  upper.CL t.ratio p.value
##  face - house      17 - 200          Left       intact    0.22438787 0.1561692 266.23 -0.08309597 0.5318717   1.437  0.1519
##  face - house      17 - 200          Right      intact    0.01316323 0.1561692 266.23 -0.29432060 0.3206471   0.084  0.9329
##  face - house      17 - 200          Left       scrambled 0.17071189 0.1675616 352.74 -0.15883355 0.5002573   1.019  0.3090
##  face - house      17 - 200          Right      scrambled 0.08086813 0.1675616 352.74 -0.24867731 0.4104136   0.483  0.6297
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95

summary(contra_all_P1_spec[1:4], delta = delta_null, side = "two.sided", adjust = "none")

##  Category_pairwise Duration_pairwise Hemisphere Type        estimate        SE     df    lower.CL  upper.CL t.ratio p.value
##  face - house      17 - 200          Left       intact    0.22438787 0.1561692 266.23 -0.08309597 0.5318717  -1.765  0.0394
##  face - house      17 - 200          Right      intact    0.01316323 0.1561692 266.23 -0.29432060 0.3206471  -3.117  0.0010
##  face - house      17 - 200          Left       scrambled 0.17071189 0.1675616 352.74 -0.15883355 0.5002573  -1.965  0.0251
##  face - house      17 - 200          Right      scrambled 0.08086813 0.1675616 352.74 -0.24867731 0.4104136  -2.501  0.0064
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

5.2.3 Ampitudes of the N170

# only keep the data for amplitudes of the N170 (all erps)
df.erp.N170.E2 <- {
  df.erp.E2.all %>% 
    filter(Component == "N170") 
}

if (saveCSV) {
  output.erp.N170.E2 <- file.path(folder_nesi, "E205_erp_N170.RData")
  save(df.erp.N170.E2, file = output.erp.N170.E2)
}

# the structure of this dataset
head(df.erp.N170.E2, 10)

## # A tibble: 10 x 64
##    Channel Event Urevent TrialNumber Block_ST_table Response urResponse    RT  urRT Reject Rejthresh Rejjp SubjCode ChanCent ChanCluster_1 ChanCluster_2 ChanCluster_3 ChanCluster_4 ChanCluster_5 ChanCluster_6 ChanCluster_7 Hemisphere Component MeanAmp MethodCode Method        IsPositive StartFrame PeakFrame EndFrame StartTime PeakTime EndTime FrameSize WindowSize StartAmplitude PeakAmplitude EndAmplitude Session Block_beha_table Trial Resp_ACC Resp_OnsetDelay Resp_RT stimName   Type   Category Duration Stimuli Type_C Cate_C Dura_C Type_Cate Type_Dura Cate_Dura Type_Cate_Dura Hemi_C Type_Hemi Cate_Hemi Dura_Hemi Type_Cate_Hemi Type_Dura_Hemi Cate_Dura_Hemi Type_Cate_Dura_Hemi
##    <chr>   <chr>   <dbl>       <dbl>          <dbl>    <dbl> <fct>      <dbl> <dbl>  <dbl>     <dbl> <dbl> <fct>    <chr>    <chr>         <chr>         <chr>         <chr>         <chr>         <chr>         <chr>         <fct>      <chr>       <dbl>      <dbl> <chr>              <dbl>      <dbl>     <dbl>    <dbl>     <dbl>    <dbl>   <dbl>     <dbl>      <dbl>          <dbl>         <dbl>        <dbl>   <dbl>            <dbl> <dbl>    <dbl>           <dbl>   <dbl> <chr>      <fct>  <fct>    <fct>    <chr>    <dbl>  <dbl>  <dbl>     <dbl>     <dbl>     <dbl>          <dbl>  <dbl>     <dbl>     <dbl>     <dbl>          <dbl>          <dbl>          <dbl>               <dbl>
##  1 E65     NF2+     2590         646              2       11 RE11         706   756      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       N170        -5.69          2 MeanAmplitude          0         38        43       48       152      172     192        10         40         -3.43         -10.2       -7.23         1               35    11        1              16     540 NfF040.bmp intact face     200      fF040     -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625
##  2 E65     NF2+     2610         651              2       11 RE11         564   614      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       N170        -2.28          2 MeanAmplitude          0         38        44       48       152      176     192        10         40         -0.667         -6.59       0.0234       1               35    16        1              17     397 NfF078.bmp intact face     200      fF078     -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625
##  3 E65     NF2+     2622         654              2       11 RE11         574   624      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       N170         3.67          2 MeanAmplitude          0         38        40       48       152      160     192        10         40          8.34           2.09       3.39         1               35    19        1              16     408 NfM122.bmp intact face     200      fM122     -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625
##  4 E65     NF2+     2626         655              2       11 RE11         387   437      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       N170         4.51          2 MeanAmplitude          0         38        41       48       152      164     192        10         40          7.44           3.17       8.05         1               35    20        1              17     220 NfM013.bmp intact face     200      fM013     -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625
##  5 E65     NF2+     2666         665              2       11 RE11         361   411      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       N170        -4.07          2 MeanAmplitude          0         38        43       48       152      172     192        10         40          2.85         -10.2       -4.62         1               35    30        1              16     195 NfM142.bmp intact face     200      fM142     -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625
##  6 E65     NF2+     2686         670              2       11 RE11         366   416      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       N170       -10.6           2 MeanAmplitude          0         38        40       48       152      160     192        10         40         -9.64         -11.6       -9.44         1               35    35        1              16     200 NfF112.bmp intact face     200      fF112     -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625
##  7 E65     NF2+     2690         671              2       11 RE11         423   473      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       N170         1.77          2 MeanAmplitude          0         38        43       48       152      172     192        10         40          0.612         -2.34       2.23         1               35    36        1              17     256 NfF017.bmp intact face     200      fF017     -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625
##  8 E65     NF2+     2722         679              2       11 RE11         446   496      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       N170         3.95          2 MeanAmplitude          0         38        41       48       152      164     192        10         40          5.19           1.21       7.96         1               35    44        1              17     279 NfF068.bmp intact face     200      fF068     -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625
##  9 E65     NF2+     2778         693              2       11 RE11         454   504      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       N170        -5.30          2 MeanAmplitude          0         38        48       48       152      192     192        10         40         -1.62          -7.56      -7.56         1               35    58        1              16     288 NfF076.bmp intact face     200      fF076     -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625
## 10 E65     NF2+     2790         696              2       11 RE11         441   491      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       N170        -1.28          2 MeanAmplitude          0         38        46       48       152      184     192        10         40         -1.24          -2.75      -1.00         1               35    61        1              16     275 NfF079.bmp intact face     200      fF079     -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625

5.2.3.1 The maximal model

file.max.N170.E2 <- file.path(folder_nesi, "E205_N170_lmm_max.RData")

# fit the maximal model
if (!file.exists(file.max.N170.E2)) {
  lmm.max.N170.E2 <- lmer(MeanAmp ~ Type * Category * Duration * Hemisphere +
                            (1 + Type_C + Cate_C + Dura_C + Hemi_C +
                               Type_Cate + Type_Dura + Cate_Dura + Type_Hemi + Cate_Hemi + Dura_Hemi +
                               Type_Cate_Dura + Type_Cate_Hemi + Type_Dura_Hemi + Cate_Dura_Hemi +
                               Type_Cate_Dura_Hemi
                             | SubjCode) +
                            (1 + Type_C + Dura_C + Hemi_C +
                               Type_Dura + Type_Hemi + Dura_Hemi +
                               Type_Dura_Hemi
                             | Stimuli),
                          data = df.erp.N170.E2,
                          verbose = TRUE,
                          control = lmerControl(optimizer = "bobyqa",  # nloptwrap Nelder_Mead
                                                optCtrl = list(maxfun = 1e7)))
} else {
  load(file.max.N170.E2)
}

print(summary(lmm.max.N170.E2), corr = FALSE)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Type * Category * Duration * Hemisphere + (1 + Type_C +      Cate_C + Dura_C + Hemi_C + Type_Cate + Type_Dura + Cate_Dura +      Type_Hemi + Cate_Hemi + Dura_Hemi + Type_Cate_Dura + Type_Cate_Hemi +      Type_Dura_Hemi + Cate_Dura_Hemi + Type_Cate_Dura_Hemi | SubjCode) +      (1 + Type_C + Dura_C + Hemi_C + Type_Dura + Type_Hemi + Dura_Hemi +          Type_Dura_Hemi | Stimuli)
##    Data: df.erp.N170.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+07))
## 
## REML criterion at convergence: 401341.2
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -7.9374 -0.5879 -0.0046  0.5793 10.5966 
## 
## Random effects:
##  Groups   Name                Variance  Std.Dev. Corr                                                                                     
##  Stimuli  (Intercept)          0.017355 0.1317                                                                                            
##           Type_C               0.099516 0.3155    0.23                                                                                    
##           Dura_C               0.031794 0.1783    0.62 -0.33                                                                              
##           Hemi_C               0.004382 0.0662    0.32  0.31 -0.14                                                                        
##           Type_Dura            0.375660 0.6129    0.30  0.42  0.13 -0.61                                                                  
##           Type_Hemi            0.002172 0.0466    0.53  0.32  0.11 -0.40  0.89                                                            
##           Dura_Hemi            0.010231 0.1011    0.49  0.63  0.04  0.87 -0.25 -0.19                                                      
##           Type_Dura_Hemi       0.040130 0.2003    0.17 -0.51  0.38 -0.76  0.55  0.63 -0.77                                                
##  SubjCode (Intercept)          3.125355 1.7679                                                                                            
##           Type_C               0.509356 0.7137   -0.23                                                                                    
##           Cate_C               0.352785 0.5940   -0.27  0.64                                                                              
##           Dura_C               0.906309 0.9520    0.00  0.20  0.02                                                                        
##           Hemi_C               1.175660 1.0843   -0.21 -0.35 -0.02 -0.34                                                                  
##           Type_Cate            1.443237 1.2013    0.62 -0.57 -0.79 -0.01 -0.06                                                            
##           Type_Dura            0.838114 0.9155   -0.04  0.59  0.36 -0.03 -0.53 -0.21                                                      
##           Cate_Dura            0.441519 0.6645    0.21  0.22  0.53 -0.20 -0.16 -0.35  0.62                                                
##           Type_Hemi            0.520714 0.7216   -0.03  0.52  0.27  0.24 -0.59 -0.27  0.43  0.21                                          
##           Cate_Hemi            0.471528 0.6867   -0.02  0.07 -0.06 -0.01 -0.42  0.02  0.24  0.15  0.83                                    
##           Dura_Hemi            1.453583 1.2056   -0.15  0.03 -0.08  0.49 -0.18  0.01  0.17  0.01  0.31  0.26                              
##           Type_Cate_Dura       1.523837 1.2344    0.39 -0.57 -0.70 -0.14  0.22  0.75 -0.62 -0.70 -0.49 -0.30 -0.14                        
##           Type_Cate_Hemi       1.545688 1.2433    0.04 -0.20  0.00 -0.11  0.42  0.08 -0.10 -0.03 -0.86 -0.95 -0.19  0.30                  
##           Type_Dura_Hemi       0.518336 0.7200    0.11  0.71  0.37 -0.08 -0.48 -0.15  0.60  0.21  0.65  0.41 -0.30 -0.34 -0.45            
##           Cate_Dura_Hemi       0.442789 0.6654    0.11 -0.17 -0.24 -0.43 -0.08  0.21  0.22  0.24  0.48  0.79 -0.08 -0.13 -0.66  0.37      
##           Type_Cate_Dura_Hemi  0.644599 0.8029    0.10 -0.36  0.05  0.29  0.24  0.00 -0.46 -0.11 -0.63 -0.70 -0.02  0.23  0.65 -0.66 -0.78
##  Residual                     16.105886 4.0132                                                                                            
## Number of obs: 71282, groups:  Stimuli, 80; SubjCode, 30
## 
## Fixed effects:
##                                                Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)                                   -1.457986   0.323589 29.121560  -4.506 9.92e-05 ***
## Type2-1                                        1.035785   0.139571 32.818293   7.421 1.64e-08 ***
## Category2-1                                    0.953981   0.117833 32.584341   8.096 2.65e-09 ***
## Duration2-1                                    0.560394   0.178509 29.688145   3.139  0.00381 ** 
## Hemisphere2-1                                  0.002074   0.201247 29.015948   0.010  0.99185    
## Type2-1:Category2-1                           -1.932099   0.241068 34.283041  -8.015 2.29e-09 ***
## Type2-1:Duration2-1                            1.014670   0.194070 36.684930   5.228 7.11e-06 ***
## Category2-1:Duration2-1                        1.064759   0.146072 32.945035   7.289 2.32e-08 ***
## Type2-1:Hemisphere2-1                          0.388565   0.149700 29.423292   2.596  0.01458 *  
## Category2-1:Hemisphere2-1                      0.188786   0.144784 30.757300   1.304  0.20194    
## Duration2-1:Hemisphere2-1                     -0.234009   0.231533 29.172466  -1.011  0.32047    
## Type2-1:Category2-1:Duration2-1               -2.037947   0.299494 42.540100  -6.805 2.60e-08 ***
## Type2-1:Category2-1:Hemisphere2-1             -0.586785   0.267832 30.765442  -2.191  0.03616 *  
## Type2-1:Duration2-1:Hemisphere2-1              0.454275   0.194635 38.826366   2.334  0.02487 *  
## Category2-1:Duration2-1:Hemisphere2-1          0.046010   0.188081 41.085375   0.245  0.80796    
## Type2-1:Category2-1:Duration2-1:Hemisphere2-1 -0.529665   0.322344 53.344936  -1.643  0.10623    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular

5.2.3.2 The zero-correlation-parameter model

file.zcp.N170.E2 <- file.path(folder_nesi, "E205_N170_lmm_zcp.RData")

# fit the zcp model
if (!file.exists(file.zcp.N170.E2)) {
  lmm.zcp.N170.E2 <- lmer(MeanAmp ~ Type * Category * Duration * Hemisphere + 
                            (1 + Type_C + Cate_C + Dura_C + Hemi_C +  
                               Type_Cate + Type_Dura + Cate_Dura + Type_Hemi + Cate_Hemi + Dura_Hemi +
                               Type_Cate_Dura + Type_Cate_Hemi + Type_Dura_Hemi + Cate_Dura_Hemi + 
                               Type_Cate_Dura_Hemi 
                             || SubjCode) +
                            (1 + Type_C + Dura_C + Hemi_C + 
                               Type_Dura + Type_Hemi + Dura_Hemi +
                               Type_Dura_Hemi 
                             || Stimuli),
                          data = df.erp.N170.E2,
                          verbose = TRUE,
                          control = lmerControl(optimizer = "bobyqa",  # nloptwrap Nelder_Mead
                                                optCtrl = list(maxfun = 1e7)))
  
} else {
  load(file.zcp.N170.E2)
}


print(summary(lmm.zcp.N170.E2), corr = FALSE)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Type * Category * Duration * Hemisphere + (1 + Type_C +      Cate_C + Dura_C + Hemi_C + Type_Cate + Type_Dura + Cate_Dura +      Type_Hemi + Cate_Hemi + Dura_Hemi + Type_Cate_Dura + Type_Cate_Hemi +      Type_Dura_Hemi + Cate_Dura_Hemi + Type_Cate_Dura_Hemi ||      SubjCode) + (1 + Type_C + Dura_C + Hemi_C + Type_Dura + Type_Hemi +      Dura_Hemi + Type_Dura_Hemi || Stimuli)
##    Data: df.erp.N170.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+07))
## 
## REML criterion at convergence: 401644.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -7.9367 -0.5852 -0.0038  0.5801 10.5465 
## 
## Random effects:
##  Groups      Name                Variance  Std.Dev. 
##  Stimuli     Type_Dura_Hemi      1.275e-13 3.571e-07
##  Stimuli.1   Dura_Hemi           2.294e-14 1.515e-07
##  Stimuli.2   Type_Hemi           0.000e+00 0.000e+00
##  Stimuli.3   Type_Dura           2.851e-01 5.340e-01
##  Stimuli.4   Hemi_C              0.000e+00 0.000e+00
##  Stimuli.5   Dura_C              8.721e-03 9.339e-02
##  Stimuli.6   Type_C              6.560e-02 2.561e-01
##  Stimuli.7   (Intercept)         1.123e-02 1.060e-01
##  SubjCode    Type_Cate_Dura_Hemi 1.072e-13 3.274e-07
##  SubjCode.1  Cate_Dura_Hemi      3.997e-03 6.322e-02
##  SubjCode.2  Type_Dura_Hemi      1.168e-01 3.417e-01
##  SubjCode.3  Type_Cate_Hemi      1.076e+00 1.037e+00
##  SubjCode.4  Type_Cate_Dura      1.287e+00 1.134e+00
##  SubjCode.5  Dura_Hemi           1.510e+00 1.229e+00
##  SubjCode.6  Cate_Hemi           2.930e-01 5.413e-01
##  SubjCode.7  Type_Hemi           3.862e-01 6.215e-01
##  SubjCode.8  Cate_Dura           4.000e-01 6.325e-01
##  SubjCode.9  Type_Dura           7.758e-01 8.808e-01
##  SubjCode.10 Type_Cate           1.289e+00 1.135e+00
##  SubjCode.11 Hemi_C              1.186e+00 1.089e+00
##  SubjCode.12 Dura_C              9.083e-01 9.530e-01
##  SubjCode.13 Cate_C              3.241e-01 5.693e-01
##  SubjCode.14 Type_C              4.811e-01 6.936e-01
##  SubjCode.15 (Intercept)         3.128e+00 1.769e+00
##  Residual                        1.614e+01 4.017e+00
## Number of obs: 71282, groups:  Stimuli, 80; SubjCode, 30
## 
## Fixed effects:
##                                                 Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)                                   -1.458e+00  3.236e-01  2.908e+01  -4.506 9.93e-05 ***
## Type2-1                                        1.034e+00  1.346e-01  3.173e+01   7.682 9.88e-09 ***
## Category2-1                                    9.531e-01  1.124e-01  3.175e+01   8.483 1.15e-09 ***
## Duration2-1                                    5.603e-01  1.779e-01  2.920e+01   3.150  0.00376 ** 
## Hemisphere2-1                                  1.826e-03  2.020e-01  2.904e+01   0.009  0.99285    
## Type2-1:Category2-1                           -1.930e+00  2.264e-01  3.295e+01  -8.524 7.62e-10 ***
## Type2-1:Duration2-1                            1.012e+00  1.856e-01  3.524e+01   5.450 4.02e-06 ***
## Category2-1:Duration2-1                        1.063e+00  1.371e-01  2.985e+01   7.748 1.25e-08 ***
## Type2-1:Hemisphere2-1                          3.885e-01  1.338e-01  3.142e+01   2.903  0.00671 ** 
## Category2-1:Hemisphere2-1                      1.895e-01  1.217e-01  3.280e+01   1.558  0.12891    
## Duration2-1:Hemisphere2-1                     -2.345e-01  2.353e-01  2.914e+01  -0.996  0.32721    
## Type2-1:Category2-1:Duration2-1               -2.036e+00  2.780e-01  4.017e+01  -7.322 6.54e-09 ***
## Type2-1:Category2-1:Hemisphere2-1             -5.890e-01  2.366e-01  3.446e+01  -2.489  0.01780 *  
## Type2-1:Duration2-1:Hemisphere2-1              4.534e-01  1.550e-01  3.135e+01   2.925  0.00636 ** 
## Category2-1:Duration2-1:Hemisphere2-1          4.961e-02  1.424e-01  3.306e+01   0.348  0.72976    
## Type2-1:Category2-1:Duration2-1:Hemisphere2-1 -5.319e-01  2.838e-01  7.052e+04  -1.874  0.06095 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular

5.2.3.3 The reduced model

summary(rePCA(lmm.zcp.N170.E2))

## $Stimuli
## Importance of components:
##                          [,1]    [,2]    [,3]    [,4]     [,5]     [,6] [,7] [,8]
## Standard deviation     0.1329 0.06376 0.02639 0.02325 8.89e-08 3.77e-08    0    0
## Proportion of Variance 0.7692 0.17698 0.03031 0.02353 0.00e+00 0.00e+00    0    0
## Cumulative Proportion  0.7692 0.94617 0.97647 1.00000 1.00e+00 1.00e+00    1    1
## 
## $SubjCode
## Importance of components:
##                          [,1]   [,2]    [,3]    [,4]    [,5]    [,6]    [,7]    [,8]    [,9]   [,10]   [,11]   [,12]   [,13]   [,14]   [,15]    [,16]
## Standard deviation     0.4403 0.3059 0.28258 0.28236 0.27113 0.25820 0.23725 0.21927 0.17267 0.15744 0.15471 0.14172 0.13475 0.08507 0.01574 8.15e-08
## Proportion of Variance 0.2376 0.1147 0.09788 0.09773 0.09011 0.08172 0.06899 0.05893 0.03655 0.03038 0.02934 0.02462 0.02226 0.00887 0.00030 0.00e+00
## Cumulative Proportion  0.2376 0.3523 0.45020 0.54793 0.63804 0.71975 0.78875 0.84768 0.88423 0.91461 0.94395 0.96857 0.99083 0.99970 1.00000 1.00e+00

file.rdc.N170.E2 <- file.path(folder_nesi, "E205_N170_lmm_rdc.RData")

# fit the rdc model
if (!file.exists(file.rdc.N170.E2)) {
  # lmm.rdc.N170.E2.step <- step(lmm.rdc.N170.E2, reduce.fixed = FALSE)
  
  lmm.rdc.N170.E2 <- update(lmm.zcp.N170.E2,
                            formula = MeanAmp ~ Type * Category * Duration * Hemisphere + 
                              (1 + Type_C + Cate_C + Dura_C + Hemi_C +  
                                 Type_Cate + Type_Dura + Cate_Dura + Type_Hemi + Cate_Hemi + Dura_Hemi +
                                 Type_Cate_Dura + Type_Cate_Hemi + Type_Dura_Hemi
                               || SubjCode) +
                              (1 + Type_C + Dura_C + 
                                 Type_Dura
                               || Stimuli))
  
} else {
  load(file.rdc.N170.E2)
}

print(summary(lmm.rdc.N170.E2), corr = FALSE)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Type + Category + Duration + Hemisphere + (1 + Type_C +      Cate_C + Dura_C + Hemi_C + Type_Cate + Type_Dura + Cate_Dura +      Type_Hemi + Cate_Hemi + Dura_Hemi + Type_Cate_Dura + Type_Cate_Hemi +      Type_Dura_Hemi || SubjCode) + (1 + Type_C + Dura_C + Type_Dura ||      Stimuli) + Type:Category + Type:Duration + Category:Duration +      Type:Hemisphere + Category:Hemisphere + Duration:Hemisphere +      Type:Category:Duration + Type:Category:Hemisphere + Type:Duration:Hemisphere +      Category:Duration:Hemisphere + Type:Category:Duration:Hemisphere
##    Data: df.erp.N170.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+07))
## 
## REML criterion at convergence: 401644.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -7.9367 -0.5852 -0.0037  0.5800 10.5465 
## 
## Random effects:
##  Groups      Name           Variance  Std.Dev.
##  Stimuli     Type_Dura       0.285125 0.53397 
##  Stimuli.1   Dura_C          0.008721 0.09339 
##  Stimuli.2   Type_C          0.065603 0.25613 
##  Stimuli.3   (Intercept)     0.011234 0.10599 
##  SubjCode    Type_Dura_Hemi  0.116720 0.34164 
##  SubjCode.1  Type_Cate_Hemi  1.075684 1.03715 
##  SubjCode.2  Type_Cate_Dura  1.286463 1.13422 
##  SubjCode.3  Dura_Hemi       1.510497 1.22902 
##  SubjCode.4  Cate_Hemi       0.292453 0.54079 
##  SubjCode.5  Type_Hemi       0.386211 0.62146 
##  SubjCode.6  Cate_Dura       0.399993 0.63245 
##  SubjCode.7  Type_Dura       0.775818 0.88081 
##  SubjCode.8  Type_Cate       1.288519 1.13513 
##  SubjCode.9  Hemi_C          1.186257 1.08915 
##  SubjCode.10 Dura_C          0.908297 0.95305 
##  SubjCode.11 Cate_C          0.324088 0.56929 
##  SubjCode.12 Type_C          0.481105 0.69362 
##  SubjCode.13 (Intercept)     3.127930 1.76860 
##  Residual                   16.137008 4.01709 
## Number of obs: 71282, groups:  Stimuli, 80; SubjCode, 30
## 
## Fixed effects:
##                                                 Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)                                   -1.458e+00  3.236e-01  2.908e+01  -4.506 9.93e-05 ***
## Type2-1                                        1.034e+00  1.346e-01  3.173e+01   7.682 9.88e-09 ***
## Category2-1                                    9.531e-01  1.124e-01  3.175e+01   8.483 1.15e-09 ***
## Duration2-1                                    5.603e-01  1.779e-01  2.920e+01   3.150  0.00376 ** 
## Hemisphere2-1                                  1.825e-03  2.020e-01  2.904e+01   0.009  0.99285    
## Type2-1:Category2-1                           -1.930e+00  2.264e-01  3.294e+01  -8.524 7.62e-10 ***
## Type2-1:Duration2-1                            1.012e+00  1.856e-01  3.524e+01   5.450 4.02e-06 ***
## Category2-1:Duration2-1                        1.063e+00  1.371e-01  2.985e+01   7.748 1.25e-08 ***
## Type2-1:Hemisphere2-1                          3.885e-01  1.338e-01  3.143e+01   2.903  0.00671 ** 
## Category2-1:Hemisphere2-1                      1.895e-01  1.216e-01  3.413e+01   1.559  0.12829    
## Duration2-1:Hemisphere2-1                     -2.345e-01  2.353e-01  2.914e+01  -0.996  0.32722    
## Type2-1:Category2-1:Duration2-1               -2.036e+00  2.780e-01  4.017e+01  -7.322 6.54e-09 ***
## Type2-1:Category2-1:Hemisphere2-1             -5.890e-01  2.366e-01  3.446e+01  -2.489  0.01780 *  
## Type2-1:Duration2-1:Hemisphere2-1              4.534e-01  1.550e-01  3.135e+01   2.925  0.00636 ** 
## Category2-1:Duration2-1:Hemisphere2-1          4.963e-02  1.419e-01  7.054e+04   0.350  0.72655    
## Type2-1:Category2-1:Duration2-1:Hemisphere2-1 -5.319e-01  2.838e-01  7.054e+04  -1.874  0.06093 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

anova(lmm.rdc.N170.E2, lmm.zcp.N170.E2, refit = FALSE)

## Data: df.erp.N170.E2
## Models:
## lmm.rdc.N170.E2: MeanAmp ~ Type + Category + Duration + Hemisphere + (1 + Type_C + 
## lmm.rdc.N170.E2:     Cate_C + Dura_C + Hemi_C + Type_Cate + Type_Dura + Cate_Dura + 
## lmm.rdc.N170.E2:     Type_Hemi + Cate_Hemi + Dura_Hemi + Type_Cate_Dura + Type_Cate_Hemi + 
## lmm.rdc.N170.E2:     Type_Dura_Hemi || SubjCode) + (1 + Type_C + Dura_C + Type_Dura || 
## lmm.rdc.N170.E2:     Stimuli) + Type:Category + Type:Duration + Category:Duration + 
## lmm.rdc.N170.E2:     Type:Hemisphere + Category:Hemisphere + Duration:Hemisphere + 
## lmm.rdc.N170.E2:     Type:Category:Duration + Type:Category:Hemisphere + Type:Duration:Hemisphere + 
## lmm.rdc.N170.E2:     Category:Duration:Hemisphere + Type:Category:Duration:Hemisphere
## lmm.zcp.N170.E2: MeanAmp ~ Type * Category * Duration * Hemisphere + (1 + Type_C + 
## lmm.zcp.N170.E2:     Cate_C + Dura_C + Hemi_C + Type_Cate + Type_Dura + Cate_Dura + 
## lmm.zcp.N170.E2:     Type_Hemi + Cate_Hemi + Dura_Hemi + Type_Cate_Dura + Type_Cate_Hemi + 
## lmm.zcp.N170.E2:     Type_Dura_Hemi + Cate_Dura_Hemi + Type_Cate_Dura_Hemi || 
## lmm.zcp.N170.E2:     SubjCode) + (1 + Type_C + Dura_C + Hemi_C + Type_Dura + Type_Hemi + 
## lmm.zcp.N170.E2:     Dura_Hemi + Type_Dura_Hemi || Stimuli)
##                 npar    AIC    BIC  logLik deviance Chisq Df Pr(>Chisq)
## lmm.rdc.N170.E2   35 401715 402036 -200822   401645                    
## lmm.zcp.N170.E2   41 401727 402103 -200822   401645 8e-04  6          1

5.2.3.4 The extended model

file.etd.N170.E2 <- file.path(folder_nesi, "E205_N170_lmm_etd.RData")

# fit the etd model
if (!file.exists(file.etd.N170.E2)) {
  
  lmm.etd.N170.E2 <- update(lmm.rdc.N170.E2,
                            formula = MeanAmp ~ Type * Category * Duration * Hemisphere + 
                              (1 + Type_C + Cate_C + Dura_C + Hemi_C +  
                                 Type_Cate + Type_Dura + Cate_Dura + Type_Hemi + Cate_Hemi + Dura_Hemi +
                                 Type_Cate_Dura + Type_Cate_Hemi + Type_Dura_Hemi
                               | SubjCode) +
                              (1 + Type_C + Dura_C + 
                                 Type_Dura
                               | Stimuli))
  
} else {
  load(file.etd.N170.E2)
}

print(summary(lmm.etd.N170.E2), corr = FALSE)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Type + Category + Duration + Hemisphere + (1 + Type_C +      Cate_C + Dura_C + Hemi_C + Type_Cate + Type_Dura + Cate_Dura +      Type_Hemi + Cate_Hemi + Dura_Hemi + Type_Cate_Dura + Type_Cate_Hemi +      Type_Dura_Hemi | SubjCode) + (1 + Type_C + Dura_C + Type_Dura |      Stimuli) + Type:Category + Type:Duration + Category:Duration +      Type:Hemisphere + Category:Hemisphere + Duration:Hemisphere +      Type:Category:Duration + Type:Category:Hemisphere + Type:Duration:Hemisphere +      Category:Duration:Hemisphere + Type:Category:Duration:Hemisphere
##    Data: df.erp.N170.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+07))
## 
## REML criterion at convergence: 401372.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -7.9468 -0.5869 -0.0042  0.5788 10.5930 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr                                                                         
##  Stimuli  (Intercept)     0.01700 0.1304                                                                                
##           Type_C          0.09751 0.3123    0.22                                                                        
##           Dura_C          0.03059 0.1749    0.63 -0.36                                                                  
##           Type_Dura       0.36712 0.6059    0.32  0.43  0.12                                                            
##  SubjCode (Intercept)     3.12552 1.7679                                                                                
##           Type_C          0.50943 0.7137   -0.23                                                                        
##           Cate_C          0.35290 0.5941   -0.27  0.64                                                                  
##           Dura_C          0.90612 0.9519    0.00  0.20  0.02                                                            
##           Hemi_C          1.17515 1.0840   -0.20 -0.35 -0.02 -0.34                                                      
##           Type_Cate       1.44334 1.2014    0.62 -0.57 -0.79 -0.01 -0.06                                                
##           Type_Dura       0.83973 0.9164   -0.04  0.59  0.36 -0.03 -0.53 -0.21                                          
##           Cate_Dura       0.44172 0.6646    0.21  0.21  0.53 -0.20 -0.16 -0.35  0.61                                    
##           Type_Hemi       0.51510 0.7177   -0.03  0.53  0.27  0.24 -0.60 -0.28  0.44  0.20                              
##           Cate_Hemi       0.30258 0.5501   -0.05  0.12  0.00  0.12 -0.49 -0.04  0.21  0.11  0.86                        
##           Dura_Hemi       1.45408 1.2059   -0.15  0.03 -0.08  0.49 -0.18  0.01  0.17  0.01  0.31  0.35                  
##           Type_Cate_Dura  1.52322 1.2342    0.39 -0.58 -0.70 -0.14  0.22  0.75 -0.62 -0.70 -0.50 -0.33 -0.14            
##           Type_Cate_Hemi  1.16352 1.0787    0.04 -0.18 -0.02 -0.22  0.43  0.11 -0.02  0.01 -0.85 -0.95 -0.22  0.29      
##           Type_Dura_Hemi  0.49472 0.7034    0.11  0.72  0.39 -0.08 -0.49 -0.16  0.61  0.20  0.64  0.37 -0.31 -0.35 -0.37
##  Residual                16.11624 4.0145                                                                                
## Number of obs: 71282, groups:  Stimuli, 80; SubjCode, 30
## 
## Fixed effects:
##                                                 Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)                                   -1.458e+00  3.236e-01  2.900e+01  -4.506 9.99e-05 ***
## Type2-1                                        1.036e+00  1.395e-01  2.389e+01   7.425 1.19e-07 ***
## Category2-1                                    9.539e-01  1.178e-01  2.705e+01   8.099 1.05e-08 ***
## Duration2-1                                    5.603e-01  1.785e-01  2.390e+01   3.140  0.00445 ** 
## Hemisphere2-1                                  1.499e-03  2.011e-01  1.289e+01   0.007  0.99417    
## Type2-1:Category2-1                           -1.932e+00  2.409e-01  2.846e+01  -8.021 8.72e-09 ***
## Type2-1:Duration2-1                            1.015e+00  1.939e-01  3.399e+01   5.232 8.60e-06 ***
## Category2-1:Duration2-1                        1.065e+00  1.459e-01  3.290e+01   7.298 2.29e-08 ***
## Type2-1:Hemisphere2-1                          3.889e-01  1.490e-01  2.525e+01   2.610  0.01501 *  
## Category2-1:Hemisphere2-1                      1.901e-01  1.229e-01  3.634e+01   1.546  0.13075    
## Duration2-1:Hemisphere2-1                     -2.345e-01  2.313e-01  1.980e+01  -1.014  0.32287    
## Type2-1:Category2-1:Duration2-1               -2.038e+00  2.988e-01  3.953e+01  -6.821 3.53e-08 ***
## Type2-1:Category2-1:Hemisphere2-1             -5.894e-01  2.427e-01  3.582e+01  -2.429  0.02031 *  
## Type2-1:Duration2-1:Hemisphere2-1              4.561e-01  1.913e-01  3.071e+01   2.384  0.02351 *  
## Category2-1:Duration2-1:Hemisphere2-1          4.947e-02  1.418e-01  7.067e+04   0.349  0.72720    
## Type2-1:Category2-1:Duration2-1:Hemisphere2-1 -5.344e-01  2.836e-01  7.067e+04  -1.884  0.05956 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular

summary(rePCA(lmm.etd.N170.E2))

## $Stimuli
## Importance of components:
##                          [,1]    [,2]    [,3]    [,4]
## Standard deviation     0.1559 0.07239 0.04451 0.01604
## Proportion of Variance 0.7647 0.16489 0.06235 0.00809
## Cumulative Proportion  0.7647 0.92956 0.99191 1.00000
## 
## $SubjCode
## Importance of components:
##                          [,1]   [,2]   [,3]    [,4]    [,5]    [,6]    [,7]    [,8]    [,9]   [,10]     [,11]     [,12]     [,13] [,14]
## Standard deviation     0.5573 0.4522 0.3638 0.28544 0.23566 0.21812 0.16008 0.13781 0.06854 0.05289 0.0001626 2.121e-06 6.643e-08     0
## Proportion of Variance 0.3513 0.2313 0.1497 0.09216 0.06282 0.05382 0.02899 0.02148 0.00531 0.00316 0.0000000 0.000e+00 0.000e+00     0
## Cumulative Proportion  0.3513 0.5826 0.7322 0.82441 0.88724 0.94105 0.97004 0.99152 0.99684 1.00000 1.0000000 1.000e+00 1.000e+00     1

file.etd1.N170.E2 <- file.path(folder_nesi, "E205_N170_lmm_etd1.RData")

# fit the etd1 model
if (!file.exists(file.etd1.N170.E2)) {
  
  lmm.etd1.N170.E2 <- update(lmm.etd.N170.E2,
                             formula = MeanAmp ~ Type * Category * Duration * Hemisphere + 
                               (1 + Type_C + Dura_C + Hemi_C +  
                                  Type_Cate + Type_Dura + Type_Hemi + Dura_Hemi +
                                  Type_Cate_Dura + Type_Cate_Hemi
                                | SubjCode) +
                               (1 + Type_C + Dura_C + 
                                  Type_Dura
                                | Stimuli))
  
} else {
  load(file.etd1.N170.E2)
}

print(summary(lmm.etd1.N170.E2), corr = FALSE)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Type + Category + Duration + Hemisphere + (1 + Type_C +      Dura_C + Hemi_C + Type_Cate + Type_Dura + Type_Hemi + Dura_Hemi +      Type_Cate_Dura + Type_Cate_Hemi | SubjCode) + (1 + Type_C +      Dura_C + Type_Dura | Stimuli) + Type:Category + Type:Duration +      Category:Duration + Type:Hemisphere + Category:Hemisphere +      Duration:Hemisphere + Type:Category:Duration + Type:Category:Hemisphere +      Type:Duration:Hemisphere + Category:Duration:Hemisphere +      Type:Category:Duration:Hemisphere
##    Data: df.erp.N170.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+07))
## 
## REML criterion at convergence: 401774.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -7.9501 -0.5858 -0.0039  0.5816 10.6037 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr                                                 
##  Stimuli  (Intercept)     0.01651 0.1285                                                        
##           Type_C          0.09623 0.3102    0.21                                                
##           Dura_C          0.03051 0.1747    0.64 -0.36                                          
##           Type_Dura       0.36483 0.6040    0.33  0.42  0.13                                    
##  SubjCode (Intercept)     3.12440 1.7676                                                        
##           Type_C          0.50975 0.7140   -0.23                                                
##           Dura_C          0.90542 0.9515    0.00  0.20                                          
##           Hemi_C          1.15652 1.0754   -0.21 -0.34 -0.34                                    
##           Type_Cate       1.75252 1.3238    0.62 -0.59 -0.03 -0.07                              
##           Type_Dura       0.84363 0.9185   -0.04  0.60 -0.03 -0.53 -0.20                        
##           Type_Hemi       0.38161 0.6177   -0.06  0.42  0.30 -0.56 -0.28  0.34                  
##           Dura_Hemi       1.48018 1.2166   -0.15  0.01  0.49 -0.17  0.03  0.15  0.43            
##           Type_Cate_Dura  1.31094 1.1450    0.30 -0.44 -0.10  0.25  0.55 -0.66 -0.49 -0.17      
##           Type_Cate_Hemi  1.69344 1.3013    0.04 -0.18 -0.21  0.46  0.09 -0.05 -0.90 -0.25  0.33
##  Residual                16.22266 4.0277                                                        
## Number of obs: 71282, groups:  Stimuli, 80; SubjCode, 30
## 
## Fixed effects:
##                                                 Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)                                   -1.457e+00  3.235e-01  2.912e+01  -4.504 9.97e-05 ***
## Type2-1                                        1.035e+00  1.395e-01  3.271e+01   7.417 1.69e-08 ***
## Category2-1                                    9.523e-01  4.574e-02  7.773e+01  20.822  < 2e-16 ***
## Duration2-1                                    5.637e-01  1.784e-01  2.968e+01   3.160  0.00362 ** 
## Hemisphere2-1                                  2.036e-03  1.995e-01  2.905e+01   0.010  0.99193    
## Type2-1:Category2-1                           -1.928e+00  2.613e-01  3.344e+01  -7.379 1.64e-08 ***
## Type2-1:Duration2-1                            1.017e+00  1.943e-01  3.637e+01   5.235 7.13e-06 ***
## Category2-1:Duration2-1                        1.060e+00  8.118e-02  7.754e+01  13.057  < 2e-16 ***
## Type2-1:Hemisphere2-1                          3.891e-01  1.333e-01  3.398e+01   2.918  0.00620 ** 
## Category2-1:Hemisphere2-1                      1.909e-01  7.114e-02  7.070e+04   2.683  0.00730 ** 
## Duration2-1:Hemisphere2-1                     -2.350e-01  2.332e-01  2.916e+01  -1.007  0.32199    
## Type2-1:Category2-1:Duration2-1               -2.031e+00  2.867e-01  4.327e+01  -7.086 9.37e-09 ***
## Type2-1:Category2-1:Hemisphere2-1             -5.876e-01  2.769e-01  3.445e+01  -2.122  0.04111 *  
## Type2-1:Duration2-1:Hemisphere2-1              4.548e-01  1.423e-01  7.070e+04   3.196  0.00139 ** 
## Category2-1:Duration2-1:Hemisphere2-1          4.671e-02  1.423e-01  7.070e+04   0.328  0.74268    
## Type2-1:Category2-1:Duration2-1:Hemisphere2-1 -5.362e-01  2.846e-01  7.070e+04  -1.884  0.05956 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular

summary(rePCA(lmm.etd1.N170.E2))

## $Stimuli
## Importance of components:
##                          [,1]    [,2]    [,3]    [,4]
## Standard deviation     0.1548 0.07198 0.04396 0.01573
## Proportion of Variance 0.7650 0.16543 0.06171 0.00790
## Cumulative Proportion  0.7650 0.93039 0.99210 1.00000
## 
## $SubjCode
## Importance of components:
##                          [,1]   [,2]   [,3]    [,4]    [,5]    [,6]   [,7]    [,8]    [,9]     [,10]
## Standard deviation     0.5368 0.4428 0.3314 0.28204 0.22279 0.21459 0.1596 0.12076 0.04202 6.325e-08
## Proportion of Variance 0.3553 0.2418 0.1354 0.09807 0.06119 0.05677 0.0314 0.01798 0.00218 0.000e+00
## Cumulative Proportion  0.3553 0.5970 0.7324 0.83048 0.89168 0.94845 0.9798 0.99782 1.00000 1.000e+00

file.etd2.N170.E2 <- file.path(folder_nesi, "E205_N170_lmm_etd2.RData")

# fit the etd2 model
if (!file.exists(file.etd2.N170.E2)) {
  
  lmm.etd2.N170.E2 <- update(lmm.etd1.N170.E2,
                             formula = MeanAmp ~ Type * Category * Duration * Hemisphere + 
                               (1 + Type_C + Dura_C + Hemi_C +  
                                  Type_Cate + Type_Dura + Dura_Hemi +
                                  Type_Cate_Dura + Type_Cate_Hemi
                                | SubjCode) +
                               (1 + Type_C + Dura_C + 
                                  Type_Dura
                                | Stimuli))
  
} else {
  load(file.etd2.N170.E2)
}

print(summary(lmm.etd2.N170.E2), corr = FALSE)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Type + Category + Duration + Hemisphere + (1 + Type_C +      Dura_C + Hemi_C + Type_Cate + Type_Dura + Dura_Hemi + Type_Cate_Dura +      Type_Cate_Hemi | SubjCode) + (1 + Type_C + Dura_C + Type_Dura |      Stimuli) + Type:Category + Type:Duration + Category:Duration +      Type:Hemisphere + Category:Hemisphere + Duration:Hemisphere +      Type:Category:Duration + Type:Category:Hemisphere + Type:Duration:Hemisphere +      Category:Duration:Hemisphere + Type:Category:Duration:Hemisphere
##    Data: df.erp.N170.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+07))
## 
## REML criterion at convergence: 401871.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -7.9303 -0.5860 -0.0048  0.5817 10.5986 
## 
## Random effects:
##  Groups   Name           Variance  Std.Dev. Corr                                           
##  Stimuli  (Intercept)     0.002805 0.05297                                                 
##           Type_C          0.092295 0.30380   1.00                                          
##           Dura_C          0.011615 0.10777  -0.32 -0.32                                    
##           Type_Dura       0.399586 0.63213   0.44  0.44  0.08                              
##  SubjCode (Intercept)     3.123796 1.76743                                                 
##           Type_C          0.509423 0.71374  -0.23                                          
##           Dura_C          0.905459 0.95156   0.00  0.20                                    
##           Hemi_C          1.218893 1.10403  -0.20 -0.35 -0.35                              
##           Type_Cate       1.753881 1.32434   0.62 -0.59 -0.03 -0.05                        
##           Type_Dura       0.838924 0.91593  -0.04  0.60 -0.03 -0.53 -0.20                  
##           Dura_Hemi       1.601393 1.26546  -0.15  0.04  0.49 -0.23  0.00  0.17            
##           Type_Cate_Dura  1.313151 1.14593   0.30 -0.44 -0.10  0.27  0.55 -0.67 -0.21      
##           Type_Cate_Hemi  1.594705 1.26282   0.04 -0.18 -0.21  0.50  0.09 -0.04 -0.31  0.36
##  Residual                16.256515 4.03194                                                 
## Number of obs: 71282, groups:  Stimuli, 80; SubjCode, 30
## 
## Fixed effects:
##                                                 Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)                                   -1.457e+00  3.232e-01  2.902e+01  -4.507 9.93e-05 ***
## Type2-1                                        1.034e+00  1.393e-01  3.248e+01   7.425 1.73e-08 ***
## Category2-1                                    9.525e-01  3.754e-02  4.821e+01  25.376  < 2e-16 ***
## Duration2-1                                    5.641e-01  1.778e-01  2.926e+01   3.174  0.00353 ** 
## Hemisphere2-1                                  2.941e-03  2.047e-01  2.905e+01   0.014  0.98864    
## Type2-1:Category2-1                           -1.929e+00  2.611e-01  3.316e+01  -7.387 1.69e-08 ***
## Type2-1:Duration2-1                            1.016e+00  1.950e-01  3.715e+01   5.208 7.32e-06 ***
## Category2-1:Duration2-1                        1.060e+00  7.520e-02  1.147e+02  14.100  < 2e-16 ***
## Type2-1:Hemisphere2-1                          3.885e-01  7.122e-02  7.080e+04   5.455 4.92e-08 ***
## Category2-1:Hemisphere2-1                      1.916e-01  7.122e-02  7.080e+04   2.690  0.00714 ** 
## Duration2-1:Hemisphere2-1                     -2.349e-01  2.418e-01  2.922e+01  -0.972  0.33917    
## Type2-1:Category2-1:Duration2-1               -2.031e+00  2.899e-01  4.504e+01  -7.006 9.93e-09 ***
## Type2-1:Category2-1:Hemisphere2-1             -5.857e-01  2.710e-01  3.421e+01  -2.161  0.03776 *  
## Type2-1:Duration2-1:Hemisphere2-1              4.493e-01  1.424e-01  7.080e+04   3.155  0.00161 ** 
## Category2-1:Duration2-1:Hemisphere2-1          4.771e-02  1.424e-01  7.080e+04   0.335  0.73767    
## Type2-1:Category2-1:Duration2-1:Hemisphere2-1 -5.345e-01  2.849e-01  7.080e+04  -1.876  0.06065 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular

summary(rePCA(lmm.etd2.N170.E2))

## $Stimuli
## Importance of components:
##                          [,1]   [,2]    [,3]      [,4]
## Standard deviation     0.1611 0.0678 0.02407 1.847e-18
## Proportion of Variance 0.8338 0.1476 0.01861 0.000e+00
## Cumulative Proportion  0.8338 0.9814 1.00000 1.000e+00
## 
## $SubjCode
## Importance of components:
##                          [,1]   [,2]   [,3]    [,4]   [,5]    [,6]    [,7]    [,8]      [,9]
## Standard deviation     0.5344 0.4352 0.3311 0.26908 0.2225 0.21374 0.15795 0.11781 2.884e-05
## Proportion of Variance 0.3610 0.2394 0.1386 0.09153 0.0626 0.05775 0.03154 0.01755 0.000e+00
## Cumulative Proportion  0.3610 0.6004 0.7390 0.83057 0.8932 0.95092 0.98245 1.00000 1.000e+00

file.etd3.N170.E2 <- file.path(folder_nesi, "E205_N170_lmm_etd3.RData")

# fit the etd3 model
if (!file.exists(file.etd3.N170.E2)) {
  
  lmm.etd3.N170.E2 <- update(lmm.etd2.N170.E2,
                             formula = MeanAmp ~ Type * Category * Duration * Hemisphere + 
                               (1 + Dura_C + Hemi_C +  
                                  Type_Cate + Type_Dura + Dura_Hemi +
                                  Type_Cate_Dura + Type_Cate_Hemi
                                | SubjCode) +
                               (0 + Type_C + Dura_C + 
                                  Type_Dura
                                | Stimuli))
  
} else {
  load(file.etd3.N170.E2)
}

print(summary(lmm.etd3.N170.E2), corr = FALSE)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Type + Category + Duration + Hemisphere + (1 + Dura_C +      Hemi_C + Type_Cate + Type_Dura + Dura_Hemi + Type_Cate_Dura +      Type_Cate_Hemi | SubjCode) + (0 + Type_C + Dura_C + Type_Dura |      Stimuli) + Type:Category + Type:Duration + Category:Duration +      Type:Hemisphere + Category:Hemisphere + Duration:Hemisphere +      Type:Category:Duration + Type:Category:Hemisphere + Type:Duration:Hemisphere +      Category:Duration:Hemisphere + Type:Category:Duration:Hemisphere
##    Data: df.erp.N170.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+07))
## 
## REML criterion at convergence: 402199
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -7.9564 -0.5894 -0.0039  0.5825 10.5129 
## 
## Random effects:
##  Groups   Name           Variance Std.Dev. Corr                                     
##  Stimuli  Type_C          0.09270 0.3045                                            
##           Dura_C          0.01805 0.1343   -0.66                                    
##           Type_Dura       0.38023 0.6166    0.41 -0.14                              
##  SubjCode (Intercept)     3.14852 1.7744                                            
##           Dura_C          0.92884 0.9638   -0.02                                    
##           Hemi_C          1.21852 1.1039   -0.19 -0.37                              
##           Type_Cate       1.75483 1.3247    0.63 -0.07 -0.05                        
##           Type_Dura       0.54862 0.7407    0.15 -0.23 -0.34  0.29                  
##           Dura_Hemi       1.60033 1.2650   -0.15  0.49 -0.23  0.00  0.17            
##           Type_Cate_Dura  1.31892 1.1484    0.30 -0.13  0.27  0.55 -0.43 -0.20      
##           Type_Cate_Hemi  1.59093 1.2613    0.05 -0.22  0.50  0.09  0.12 -0.31  0.36
##  Residual                16.34600 4.0430                                            
## Number of obs: 71282, groups:  Stimuli, 80; SubjCode, 30
## 
## Fixed effects:
##                                                 Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)                                   -1.456e+00  3.245e-01  2.900e+01  -4.488 0.000105 ***
## Type2-1                                        1.031e+00  4.934e-02  7.879e+01  20.905  < 2e-16 ***
## Category2-1                                    9.524e-01  3.571e-02  7.090e+04  26.665  < 2e-16 ***
## Duration2-1                                    5.661e-01  1.802e-01  2.942e+01   3.142 0.003812 ** 
## Hemisphere2-1                                  2.973e-03  2.047e-01  2.905e+01   0.015 0.988510    
## Type2-1:Category2-1                           -1.924e+00  2.612e-01  3.328e+01  -7.367 1.76e-08 ***
## Type2-1:Duration2-1                            1.011e+00  1.678e-01  4.324e+01   6.025 3.30e-07 ***
## Category2-1:Duration2-1                        1.060e+00  7.749e-02  1.154e+02  13.685  < 2e-16 ***
## Type2-1:Hemisphere2-1                          3.885e-01  7.142e-02  7.083e+04   5.439 5.36e-08 ***
## Category2-1:Hemisphere2-1                      1.916e-01  7.141e-02  7.083e+04   2.683 0.007308 ** 
## Duration2-1:Hemisphere2-1                     -2.349e-01  2.418e-01  2.921e+01  -0.972 0.339161    
## Type2-1:Category2-1:Duration2-1               -2.021e+00  2.888e-01  4.417e+01  -7.000 1.12e-08 ***
## Type2-1:Category2-1:Hemisphere2-1             -5.858e-01  2.710e-01  3.424e+01  -2.162 0.037722 *  
## Type2-1:Duration2-1:Hemisphere2-1              4.492e-01  1.428e-01  7.083e+04   3.145 0.001661 ** 
## Category2-1:Duration2-1:Hemisphere2-1          4.772e-02  1.428e-01  7.083e+04   0.334 0.738281    
## Type2-1:Category2-1:Duration2-1:Hemisphere2-1 -5.343e-01  2.857e-01  7.083e+04  -1.870 0.061423 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

anova(lmm.etd3.N170.E2, lmm.rdc.N170.E2, refit = FALSE)

## Data: df.erp.N170.E2
## Models:
## lmm.rdc.N170.E2: MeanAmp ~ Type + Category + Duration + Hemisphere + (1 + Type_C + 
## lmm.rdc.N170.E2:     Cate_C + Dura_C + Hemi_C + Type_Cate + Type_Dura + Cate_Dura + 
## lmm.rdc.N170.E2:     Type_Hemi + Cate_Hemi + Dura_Hemi + Type_Cate_Dura + Type_Cate_Hemi + 
## lmm.rdc.N170.E2:     Type_Dura_Hemi || SubjCode) + (1 + Type_C + Dura_C + Type_Dura || 
## lmm.rdc.N170.E2:     Stimuli) + Type:Category + Type:Duration + Category:Duration + 
## lmm.rdc.N170.E2:     Type:Hemisphere + Category:Hemisphere + Duration:Hemisphere + 
## lmm.rdc.N170.E2:     Type:Category:Duration + Type:Category:Hemisphere + Type:Duration:Hemisphere + 
## lmm.rdc.N170.E2:     Category:Duration:Hemisphere + Type:Category:Duration:Hemisphere
## lmm.etd3.N170.E2: MeanAmp ~ Type + Category + Duration + Hemisphere + (1 + Dura_C + 
## lmm.etd3.N170.E2:     Hemi_C + Type_Cate + Type_Dura + Dura_Hemi + Type_Cate_Dura + 
## lmm.etd3.N170.E2:     Type_Cate_Hemi | SubjCode) + (0 + Type_C + Dura_C + Type_Dura | 
## lmm.etd3.N170.E2:     Stimuli) + Type:Category + Type:Duration + Category:Duration + 
## lmm.etd3.N170.E2:     Type:Hemisphere + Category:Hemisphere + Duration:Hemisphere + 
## lmm.etd3.N170.E2:     Type:Category:Duration + Type:Category:Hemisphere + Type:Duration:Hemisphere + 
## lmm.etd3.N170.E2:     Category:Duration:Hemisphere + Type:Category:Duration:Hemisphere
##                  npar    AIC    BIC  logLik deviance Chisq Df Pr(>Chisq)
## lmm.rdc.N170.E2    35 401715 402036 -200822   401645                    
## lmm.etd3.N170.E2   59 402317 402858 -201099   402199     0 24          1

5.2.3.5 The optimal model

lmm.opt.N170.E205 <- lmm.rdc.N170.E2

print(summary(lmm.opt.N170.E205), corr = FALSE)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Type + Category + Duration + Hemisphere + (1 + Type_C +      Cate_C + Dura_C + Hemi_C + Type_Cate + Type_Dura + Cate_Dura +      Type_Hemi + Cate_Hemi + Dura_Hemi + Type_Cate_Dura + Type_Cate_Hemi +      Type_Dura_Hemi || SubjCode) + (1 + Type_C + Dura_C + Type_Dura ||      Stimuli) + Type:Category + Type:Duration + Category:Duration +      Type:Hemisphere + Category:Hemisphere + Duration:Hemisphere +      Type:Category:Duration + Type:Category:Hemisphere + Type:Duration:Hemisphere +      Category:Duration:Hemisphere + Type:Category:Duration:Hemisphere
##    Data: df.erp.N170.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+07))
## 
## REML criterion at convergence: 401644.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -7.9367 -0.5852 -0.0037  0.5800 10.5465 
## 
## Random effects:
##  Groups      Name           Variance  Std.Dev.
##  Stimuli     Type_Dura       0.285125 0.53397 
##  Stimuli.1   Dura_C          0.008721 0.09339 
##  Stimuli.2   Type_C          0.065603 0.25613 
##  Stimuli.3   (Intercept)     0.011234 0.10599 
##  SubjCode    Type_Dura_Hemi  0.116720 0.34164 
##  SubjCode.1  Type_Cate_Hemi  1.075684 1.03715 
##  SubjCode.2  Type_Cate_Dura  1.286463 1.13422 
##  SubjCode.3  Dura_Hemi       1.510497 1.22902 
##  SubjCode.4  Cate_Hemi       0.292453 0.54079 
##  SubjCode.5  Type_Hemi       0.386211 0.62146 
##  SubjCode.6  Cate_Dura       0.399993 0.63245 
##  SubjCode.7  Type_Dura       0.775818 0.88081 
##  SubjCode.8  Type_Cate       1.288519 1.13513 
##  SubjCode.9  Hemi_C          1.186257 1.08915 
##  SubjCode.10 Dura_C          0.908297 0.95305 
##  SubjCode.11 Cate_C          0.324088 0.56929 
##  SubjCode.12 Type_C          0.481105 0.69362 
##  SubjCode.13 (Intercept)     3.127930 1.76860 
##  Residual                   16.137008 4.01709 
## Number of obs: 71282, groups:  Stimuli, 80; SubjCode, 30
## 
## Fixed effects:
##                                                 Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)                                   -1.458e+00  3.236e-01  2.908e+01  -4.506 9.93e-05 ***
## Type2-1                                        1.034e+00  1.346e-01  3.173e+01   7.682 9.88e-09 ***
## Category2-1                                    9.531e-01  1.124e-01  3.175e+01   8.483 1.15e-09 ***
## Duration2-1                                    5.603e-01  1.779e-01  2.920e+01   3.150  0.00376 ** 
## Hemisphere2-1                                  1.825e-03  2.020e-01  2.904e+01   0.009  0.99285    
## Type2-1:Category2-1                           -1.930e+00  2.264e-01  3.294e+01  -8.524 7.62e-10 ***
## Type2-1:Duration2-1                            1.012e+00  1.856e-01  3.524e+01   5.450 4.02e-06 ***
## Category2-1:Duration2-1                        1.063e+00  1.371e-01  2.985e+01   7.748 1.25e-08 ***
## Type2-1:Hemisphere2-1                          3.885e-01  1.338e-01  3.143e+01   2.903  0.00671 ** 
## Category2-1:Hemisphere2-1                      1.895e-01  1.216e-01  3.413e+01   1.559  0.12829    
## Duration2-1:Hemisphere2-1                     -2.345e-01  2.353e-01  2.914e+01  -0.996  0.32722    
## Type2-1:Category2-1:Duration2-1               -2.036e+00  2.780e-01  4.017e+01  -7.322 6.54e-09 ***
## Type2-1:Category2-1:Hemisphere2-1             -5.890e-01  2.366e-01  3.446e+01  -2.489  0.01780 *  
## Type2-1:Duration2-1:Hemisphere2-1              4.534e-01  1.550e-01  3.135e+01   2.925  0.00636 ** 
## Category2-1:Duration2-1:Hemisphere2-1          4.963e-02  1.419e-01  7.054e+04   0.350  0.72655    
## Type2-1:Category2-1:Duration2-1:Hemisphere2-1 -5.319e-01  2.838e-01  7.054e+04  -1.874  0.06093 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.2.3.6 Diagnostic plots

# qqplot
qqplot_lmer(lmm.opt.N170.E205)

5.2.3.7 Estimated marginal means

emm.N170.E205 <- emmeans(lmm.opt.N170.E205,  ~ Type + Hemisphere + Category + Duration)

emm.N170.E205 %>% 
  as.data.frame() %>% 
  arrange(Type, Hemisphere, Category, Duration)

##         Type Hemisphere Category Duration     emmean        SE       df  lower.CL   upper.CL
## 1     intact       Left     face       17 -2.3789018 0.3773681 52.69507 -3.135909 -1.6218949
## 2     intact       Left     face      200 -3.0552435 0.3845664 56.83221 -3.825374 -2.2851133
## 3     intact       Left    house       17 -1.6639996 0.3773589 52.68993 -2.420990 -0.9070095
## 4     intact       Left    house      200 -0.4177229 0.3846839 56.90173 -1.188068  0.3526222
## 5     intact      Right     face       17 -2.5037987 0.3773681 52.69507 -3.260806 -1.7467919
## 6     intact      Right     face      200 -3.7991793 0.3845664 56.83221 -4.569310 -3.0290492
## 7     intact      Right    house       17 -1.4626751 0.3773589 52.68993 -2.219665 -0.7056849
## 8     intact      Right    house      200 -0.5198350 0.3846839 56.90173 -1.290180  0.2505101
## 9  scrambled       Left     face       17 -1.5562636 0.3797770 54.05350 -2.317653 -0.7948739
## 10 scrambled       Left     face      200 -0.5626092 0.3847237 56.92527 -1.333027  0.2078087
## 11 scrambled       Left    house       17 -1.5921698 0.3797782 54.05420 -2.353562 -0.8307778
## 12 scrambled       Left    house      200 -0.4456271 0.3847015 56.91213 -1.216004  0.3247502
## 13 scrambled      Right     face       17 -1.3579099 0.3797770 54.05350 -2.119300 -0.5965202
## 14 scrambled      Right     face      200 -0.2638789 0.3847237 56.92527 -1.034297  0.5065390
## 15 scrambled      Right    house       17 -1.3906216 0.3797782 54.05420 -2.152014 -0.6292296
## 16 scrambled      Right    house      200 -0.3600356 0.3847015 56.91213 -1.130413  0.4103417

5.2.3.8 Plots

# emmip(emm.N170.E205, Category ~ Duration | Type + Hemisphere, CIs = TRUE) + 
#   scale_y_continuous(trans = "reverse")

n170.all.LinePlot = {
  ggplot(data = data.frame(emm.N170.E205), aes(y = emmean, x = Duration, color = Category, group = Category)) +  # set the data, varialbes for x and y axises, and the variable for dividing data
    geom_point(size = 2) +
    geom_line(aes(linetype = Category), size = .85) +  # position = "dodge", alpha = .7
    scale_linetype_manual(values=c("solid", "dashed")) +
    geom_errorbar(mapping = aes(ymin = lower.CL, ymax = upper.CL), linetype = 1,  # set the error bar
                  show.legend = FALSE, width = 0.25, alpha = .5) + # ,                   position = position_dodge(width=0.9)
    facet_grid(Type ~ Hemisphere, # switch = "x",
               labeller = labeller(Hemisphere = hemiLabel)) +
    scale_y_reverse(limits = ylimit_n170, breaks = seq(1, -5, -1)) +  # set the limit for y axis
    # scale_y_continuous(expand= c(0, 0)) +  # remove the space between columns and x axis
    labs(x = "Duration (ms)", y = expression(paste("Amplitudes (", mu, "V)")), fill = "Category") +  # set the names for main, x and y axises title = "", 
    # scale_x_discrete(labels = c("left", "right")) +
    # geom_text(label = c("", "", "", "", "***", "", "", ""), color = "red", size = 6, nudge_y = 0.5, nudge_x = 0.5) + # add starts to the significant columns
    theme_bw() +
    erp_theme 
}

# n170.all.LinePlot.slide <- {
#   n170.all.LinePlot + 
#     scale_y_reverse(limits = c(0.8, -4.8), breaks = seq(0, -4, -1)) +  # set the limit for y axis
#     theme(legend.position="right",
#           axis.line = element_line(color="black", size = 3))
# }
# ggsave('N170_all.png', n170.all.LinePlot.slide, width = 7, height = 4)

n170.all.LinePlot

n170_all_LinePlot <- {
  ggplot(data = data.frame(emm.N170.E205), aes(y = emmean, x = Duration, group = Category)) +  # set the data, varialbes for x and y axises, and the variable for dividing data
    geom_point(aes(fill = Duration), size=4, shape=21, stroke=0) +
    scale_fill_manual(values = duration_color[c(3, 4)], guide = FALSE) +
    geom_line(aes(linetype = Category, color = Category),  size = .85) +  # position = "dodge", alpha = .7
    scale_color_manual(values=category_color, limits = c("face", "house")) +
    scale_linetype_manual(values=c("solid", "dashed")) +
    geom_errorbar(mapping = aes(ymin = lower.CL, ymax = upper.CL), linetype = 1,  # set the error bar
                  show.legend = FALSE, width = 0.25, alpha = .5) + # ,                   position = position_dodge(width=0.9)
    facet_grid(Type ~ Hemisphere, # switch = "x",
               labeller = labeller(Hemisphere = hemiLabel)) +
    scale_y_reverse(limits = ylimit_n170, breaks = seq(1, -5, -1)) +  # set the limit for y axis
    # scale_y_continuous(expand= c(0, 0)) +  # remove the space between columns and x axis
    labs(x = "Duration (ms)", y = expression(paste("N170 Amplitudes (", mu, "V)")), color = "Category", linetype = "Category") +  # set the names for main, x and y axises title = "", 
    # scale_x_discrete(labels = c("left", "right")) +
    # geom_text(label = c("", "", "", "", "***", "", "", ""), color = "red", size = 6, nudge_y = 0.5, nudge_x = 0.5) + # add starts to the significant columns
    theme_bw() +
    erp_theme +
    theme(
      legend.position = c(.5, .5),
      legend.title=element_text(size=10), 
      legend.text=element_text(size=10),
    ) +
    guides(linetype = guide_legend(title.position = "left", nrow = 1),
           color = guide_legend(title.position = "left", nrow = 1))
}

ggsave('N170_all_pub.pdf', n170_all_LinePlot, width = 6, height = 5)

5.2.3.9 Contrasts

5.2.3.9.1 Main results for intact stimuli

Comparisons of N170 amplitudes for intact faces with different durations:

contra_all_N170 <- contrast(emmeans(lmm.opt.N170.E205,  ~ Duration | Hemisphere + Category + Type),
                            "pairwise", adjust = "Bonferroni", infer = TRUE)

contra_all_N170[1:2]

##  contrast Hemisphere Category Type    estimate        SE     df  lower.CL upper.CL t.ratio p.value
##  17 - 200 Left       face     intact 0.6763417 0.2582103 100.06 0.0887499 1.263934   2.619  0.0204
##  17 - 200 Right      face     intact 1.2953806 0.2582103 100.06 0.7077888 1.882972   5.017  <.0001
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 2 estimates 
## P value adjustment: bonferroni method for 2 tests

summary(contra_all_N170[1:2], delta = delta_null, level = .9)

##  contrast Hemisphere Category Type    estimate        SE     df  lower.CL upper.CL t.ratio p.value
##  17 - 200 Left       face     intact 0.6763417 0.2582103 100.06 0.1640634 1.188620   0.683  1.0000
##  17 - 200 Right      face     intact 1.2953806 0.2582103 100.06 0.7831023 1.807659   3.080  1.0000
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Conf-level adjustment: bonferroni method for 2 estimates 
## P value adjustment: bonferroni method for 2 tests 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

summary(contra_all_N170[1:2], delta = delta_null, side = ">")

##  contrast Hemisphere Category Type    estimate        SE     df  lower.CL upper.CL t.ratio p.value
##  17 - 200 Left       face     intact 0.6763417 0.2582103 100.06 0.1640634      Inf   4.556  <.0001
##  17 - 200 Right      face     intact 1.2953806 0.2582103 100.06 0.7831023      Inf   6.953  <.0001
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 2 estimates 
## P value adjustment: bonferroni method for 2 tests 
## Statistics are tests of noninferiority with a threshold of 0.5 
## P values are right-tailed

summary(contra_all_N170[1:2], delta = delta_null, side = "<")

##  contrast Hemisphere Category Type    estimate        SE     df lower.CL upper.CL t.ratio p.value
##  17 - 200 Left       face     intact 0.6763417 0.2582103 100.06     -Inf 1.188620   0.683  1.0000
##  17 - 200 Right      face     intact 1.2953806 0.2582103 100.06     -Inf 1.807659   3.080  1.0000
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 2 estimates 
## P value adjustment: bonferroni method for 2 tests 
## Statistics are tests of nonsuperiority with a threshold of 0.5 
## P values are left-tailed

Comparisons of N170 amplitude lateralization for intact faces with different durations:

contra_all_N170_hemi <- contrast(emmeans(lmm.opt.N170.E205, ~ Duration + Hemisphere | Category + Type), 
                                 interaction = "pairwise", infer = TRUE)
contra_all_N170_hemi[1]

##  Duration_pairwise Hemisphere_pairwise Category Type     estimate        SE    df  lower.CL   upper.CL t.ratio p.value
##  17 - 200          Left - Right        face     intact -0.6190389 0.2636081 45.58 -1.149785 -0.0882927  -2.348  0.0233
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95

summary(contra_all_N170_hemi[1], delta = delta_null, level = .9)

##  Duration_pairwise Hemisphere_pairwise Category Type     estimate        SE    df lower.CL   upper.CL t.ratio p.value
##  17 - 200          Left - Right        face     intact -0.6190389 0.2636081 45.58 -1.06163 -0.1764475   0.452  0.6731
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

summary(contra_all_N170_hemi[1], delta = delta_null, side = ">")

##  Duration_pairwise Hemisphere_pairwise Category Type     estimate        SE    df lower.CL upper.CL t.ratio p.value
##  17 - 200          Left - Right        face     intact -0.6190389 0.2636081 45.58 -1.06163      Inf  -0.452  0.6731
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Statistics are tests of noninferiority with a threshold of 0.5 
## P values are right-tailed

summary(contra_all_N170_hemi[1], delta = delta_null, side = "<")

##  Duration_pairwise Hemisphere_pairwise Category Type     estimate        SE    df lower.CL   upper.CL t.ratio p.value
##  17 - 200          Left - Right        face     intact -0.6190389 0.2636081 45.58     -Inf -0.1764475  -4.245  0.0001
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Statistics are tests of nonsuperiority with a threshold of 0.5 
## P values are left-tailed

Comparisons of face-specific N170 amplitude components for intact stimuli with different durations:

contra_all_N170_spec <- contrast(emmeans(lmm.opt.N170.E205, ~ Category + Duration | Hemisphere + Type), 
                                 interaction = "pairwise", adjust = "Bonferroni", infer = TRUE)

contra_all_N170_spec[1:2]

##  Category_pairwise Duration_pairwise Hemisphere Type   estimate        SE     df lower.CL upper.CL t.ratio p.value
##  face - house      17 - 200          Left       intact 1.922618 0.2151955 104.11 1.433203 2.412034   8.934  <.0001
##  face - house      17 - 200          Right      intact 2.238221 0.2151955 104.11 1.748805 2.727636  10.401  <.0001
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 2 estimates 
## P value adjustment: bonferroni method for 2 tests

summary(contra_all_N170_spec[1:2], delta = delta_null, level = .9)

##  Category_pairwise Duration_pairwise Hemisphere Type   estimate        SE     df lower.CL upper.CL t.ratio p.value
##  face - house      17 - 200          Left       intact 1.922618 0.2151955 104.11 1.495883 2.349354   6.611  1.0000
##  face - house      17 - 200          Right      intact 2.238221 0.2151955 104.11 1.811485 2.664956   8.077  1.0000
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Conf-level adjustment: bonferroni method for 2 estimates 
## P value adjustment: bonferroni method for 2 tests 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

summary(contra_all_N170_spec[1:2], delta = delta_null, side = ">")

##  Category_pairwise Duration_pairwise Hemisphere Type   estimate        SE     df lower.CL upper.CL t.ratio p.value
##  face - house      17 - 200          Left       intact 1.922618 0.2151955 104.11 1.495883      Inf  11.258  <.0001
##  face - house      17 - 200          Right      intact 2.238221 0.2151955 104.11 1.811485      Inf  12.724  <.0001
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 2 estimates 
## P value adjustment: bonferroni method for 2 tests 
## Statistics are tests of noninferiority with a threshold of 0.5 
## P values are right-tailed

summary(contra_all_N170_spec[1:2], delta = delta_null, side = "<")

##  Category_pairwise Duration_pairwise Hemisphere Type   estimate        SE     df lower.CL upper.CL t.ratio p.value
##  face - house      17 - 200          Left       intact 1.922618 0.2151955 104.11     -Inf 2.349354   6.611  1.0000
##  face - house      17 - 200          Right      intact 2.238221 0.2151955 104.11     -Inf 2.664956   8.077  1.0000
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 2 estimates 
## P value adjustment: bonferroni method for 2 tests 
## Statistics are tests of nonsuperiority with a threshold of 0.5 
## P values are left-tailed

Comparisons of face-specific N170 amplitude component lateralization for intact stimuli with different durations:

contra_all_N170_spec_hemi <- contrast(emmeans(lmm.opt.N170.E205, ~ Category + Duration + Hemisphere | Type), 
                                 interaction = "pairwise", adjust = "Bonferroni", infer = TRUE)

contra_all_N170_spec_hemi[1]

##  Category_pairwise Duration_pairwise Hemisphere_pairwise Type     estimate        SE       df   lower.CL   upper.CL t.ratio p.value
##  face - house      17 - 200          Left - Right        intact -0.3156022 0.1910371 70540.74 -0.6900345 0.05883006  -1.652  0.0985
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95

summary(contra_all_N170_spec_hemi[1], delta = delta_null, level = .9)

##  Category_pairwise Duration_pairwise Hemisphere_pairwise Type     estimate        SE       df   lower.CL     upper.CL t.ratio p.value
##  face - house      17 - 200          Left - Right        intact -0.3156022 0.1910371 70540.74 -0.6298344 -0.001370009  -0.965  0.1672
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

summary(contra_all_N170_spec_hemi[1], delta = delta_null, side = ">")

##  Category_pairwise Duration_pairwise Hemisphere_pairwise Type     estimate        SE       df   lower.CL upper.CL t.ratio p.value
##  face - house      17 - 200          Left - Right        intact -0.3156022 0.1910371 70540.74 -0.6298344      Inf   0.965  0.1672
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Statistics are tests of noninferiority with a threshold of 0.5 
## P values are right-tailed

summary(contra_all_N170_spec_hemi[1], delta = delta_null, side = "<")

##  Category_pairwise Duration_pairwise Hemisphere_pairwise Type     estimate        SE       df lower.CL     upper.CL t.ratio p.value
##  face - house      17 - 200          Left - Right        intact -0.3156022 0.1910371 70540.74     -Inf -0.001370009  -4.269  <.0001
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Statistics are tests of nonsuperiority with a threshold of 0.5 
## P values are left-tailed

5.2.3.9.2 Main results for scrambled stimuli

Comparisons of N170 amplitudes for scrambled faces with different durations:

contra_all_N170[5:6]

##  contrast Hemisphere Category Type        estimate        SE     df  lower.CL   upper.CL t.ratio p.value
##  17 - 200 Left       face     scrambled -0.9936544 0.2619497 105.98 -1.589248 -0.3980611  -3.793  0.0005
##  17 - 200 Right      face     scrambled -1.0940310 0.2619497 105.98 -1.689624 -0.4984377  -4.176  0.0001
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 2 estimates 
## P value adjustment: bonferroni method for 2 tests

summary(contra_all_N170[5:6], delta = delta_null, level = .9)

##  contrast Hemisphere Category Type        estimate        SE     df  lower.CL   upper.CL t.ratio p.value
##  17 - 200 Left       face     scrambled -0.9936544 0.2619497 105.98 -1.512996 -0.4743125   1.885  1.0000
##  17 - 200 Right      face     scrambled -1.0940310 0.2619497 105.98 -1.613373 -0.5746891   2.268  1.0000
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Conf-level adjustment: bonferroni method for 2 estimates 
## P value adjustment: bonferroni method for 2 tests 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

summary(contra_all_N170[5:6], delta = delta_null, side = ">")

##  contrast Hemisphere Category Type        estimate        SE     df  lower.CL upper.CL t.ratio p.value
##  17 - 200 Left       face     scrambled -0.9936544 0.2619497 105.98 -1.512996      Inf  -1.885  1.0000
##  17 - 200 Right      face     scrambled -1.0940310 0.2619497 105.98 -1.613373      Inf  -2.268  1.0000
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 2 estimates 
## P value adjustment: bonferroni method for 2 tests 
## Statistics are tests of noninferiority with a threshold of 0.5 
## P values are right-tailed

summary(contra_all_N170[5:6], delta = delta_null, side = "<")

##  contrast Hemisphere Category Type        estimate        SE     df lower.CL   upper.CL t.ratio p.value
##  17 - 200 Left       face     scrambled -0.9936544 0.2619497 105.98     -Inf -0.4743125  -5.702  <.0001
##  17 - 200 Right      face     scrambled -1.0940310 0.2619497 105.98     -Inf -0.5746891  -6.085  <.0001
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 2 estimates 
## P value adjustment: bonferroni method for 2 tests 
## Statistics are tests of nonsuperiority with a threshold of 0.5 
## P values are left-tailed

Comparisons of N170 amplitude lateralization for scrambled faces with different durations:

contra_all_N170_hemi[3]

##  Duration_pairwise Hemisphere_pairwise Category Type       estimate        SE    df   lower.CL  upper.CL t.ratio p.value
##  17 - 200          Left - Right        face     scrambled 0.1003766 0.2708823 50.83 -0.4434872 0.6442404   0.371  0.7125
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95

summary(contra_all_N170_hemi[3], delta = delta_null, level = .9)

##  Duration_pairwise Hemisphere_pairwise Category Type       estimate        SE    df  lower.CL  upper.CL t.ratio p.value
##  17 - 200          Left - Right        face     scrambled 0.1003766 0.2708823 50.83 -0.353457 0.5542102  -1.475  0.0732
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

summary(contra_all_N170_hemi[3], delta = delta_null, side = ">")

##  Duration_pairwise Hemisphere_pairwise Category Type       estimate        SE    df  lower.CL upper.CL t.ratio p.value
##  17 - 200          Left - Right        face     scrambled 0.1003766 0.2708823 50.83 -0.353457      Inf   2.216  0.0156
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Statistics are tests of noninferiority with a threshold of 0.5 
## P values are right-tailed

summary(contra_all_N170_hemi[3], delta = delta_null, side = "<")

##  Duration_pairwise Hemisphere_pairwise Category Type       estimate        SE    df lower.CL  upper.CL t.ratio p.value
##  17 - 200          Left - Right        face     scrambled 0.1003766 0.2708823 50.83     -Inf 0.5542102  -1.475  0.0732
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Statistics are tests of nonsuperiority with a threshold of 0.5 
## P values are left-tailed

Comparisons of face-specific N170 amplitude components for scrambled stimuli with different durations:

contra_all_N170_spec[3:4]

##  Category_pairwise Duration_pairwise Hemisphere Type         estimate        SE     df   lower.CL  upper.CL t.ratio p.value
##  face - house      17 - 200          Left       scrambled  0.15288823 0.2238302 121.85 -0.3550805 0.6608569   0.683  0.9917
##  face - house      17 - 200          Right      scrambled -0.06344508 0.2238302 121.85 -0.5714138 0.4445236  -0.283  1.0000
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 2 estimates 
## P value adjustment: bonferroni method for 2 tests

summary(contra_all_N170_spec[3:4], delta = delta_null, level = .9)

##  Category_pairwise Duration_pairwise Hemisphere Type         estimate        SE     df   lower.CL  upper.CL t.ratio p.value
##  face - house      17 - 200          Left       scrambled  0.15288823 0.2238302 121.85 -0.2902115 0.5959880  -1.551  0.1235
##  face - house      17 - 200          Right      scrambled -0.06344508 0.2238302 121.85 -0.5065448 0.3796547  -1.950  0.0534
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Conf-level adjustment: bonferroni method for 2 estimates 
## P value adjustment: bonferroni method for 2 tests 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

summary(contra_all_N170_spec[3:4], delta = delta_null, side = ">")

##  Category_pairwise Duration_pairwise Hemisphere Type         estimate        SE     df   lower.CL upper.CL t.ratio p.value
##  face - house      17 - 200          Left       scrambled  0.15288823 0.2238302 121.85 -0.2902115      Inf   2.917  0.0042
##  face - house      17 - 200          Right      scrambled -0.06344508 0.2238302 121.85 -0.5065448      Inf   1.950  0.0534
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 2 estimates 
## P value adjustment: bonferroni method for 2 tests 
## Statistics are tests of noninferiority with a threshold of 0.5 
## P values are right-tailed

summary(contra_all_N170_spec[3:4], delta = delta_null, side = "<")

##  Category_pairwise Duration_pairwise Hemisphere Type         estimate        SE     df lower.CL  upper.CL t.ratio p.value
##  face - house      17 - 200          Left       scrambled  0.15288823 0.2238302 121.85     -Inf 0.5959880  -1.551  0.1235
##  face - house      17 - 200          Right      scrambled -0.06344508 0.2238302 121.85     -Inf 0.3796547  -2.517  0.0131
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 2 estimates 
## P value adjustment: bonferroni method for 2 tests 
## Statistics are tests of nonsuperiority with a threshold of 0.5 
## P values are left-tailed

5.2.3.9.3 Other results

Comparisons of N170 amplitudes between Durations (without multiple comparison corrections):

summary(contra_all_N170[1:8], adjust = "none")

##  contrast Hemisphere Category Type        estimate        SE     df   lower.CL   upper.CL t.ratio p.value
##  17 - 200 Left       face     intact     0.6763417 0.2582103 100.06  0.1640634  1.1886201   2.619  0.0102
##  17 - 200 Right      face     intact     1.2953806 0.2582103 100.06  0.7831023  1.8076590   5.017  <.0001
##  17 - 200 Left       house    intact    -1.2462767 0.2583720 100.31 -1.7588602 -0.7336932  -4.824  <.0001
##  17 - 200 Right      house    intact    -0.9428400 0.2583720 100.31 -1.4554235 -0.4302565  -3.649  0.0004
##  17 - 200 Left       face     scrambled -0.9936544 0.2619497 105.98 -1.5129963 -0.4743125  -3.793  0.0002
##  17 - 200 Right      face     scrambled -1.0940310 0.2619497 105.98 -1.6133729 -0.5746891  -4.176  0.0001
##  17 - 200 Left       house    scrambled -1.1465426 0.2619183 105.93 -1.6658252 -0.6272601  -4.377  <.0001
##  17 - 200 Right      house    scrambled -1.0305859 0.2619183 105.93 -1.5498685 -0.5113034  -3.935  0.0001
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95

summary(contra_all_N170[1:8], delta = delta_null, level = .9, adjust = "none")

##  contrast Hemisphere Category Type        estimate        SE     df   lower.CL   upper.CL t.ratio p.value
##  17 - 200 Left       face     intact     0.6763417 0.2582103 100.06  0.2476544  1.1050290   0.683  0.7519
##  17 - 200 Right      face     intact     1.2953806 0.2582103 100.06  0.8666933  1.7240680   3.080  0.9987
##  17 - 200 Left       house    intact    -1.2462767 0.2583720 100.31 -1.6752225 -0.8173309   2.888  0.9976
##  17 - 200 Right      house    intact    -0.9428400 0.2583720 100.31 -1.3717858 -0.5138942   1.714  0.9552
##  17 - 200 Left       face     scrambled -0.9936544 0.2619497 105.98 -1.4283229 -0.5589859   1.885  0.9689
##  17 - 200 Right      face     scrambled -1.0940310 0.2619497 105.98 -1.5286996 -0.6593625   2.268  0.9873
##  17 - 200 Left       house    scrambled -1.1465426 0.2619183 105.93 -1.5811609 -0.7119243   2.468  0.9924
##  17 - 200 Right      house    scrambled -1.0305859 0.2619183 105.93 -1.4652042 -0.5959676   2.026  0.9773
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

Comparisons of N170 amplitude lateralization between Durations (without multiple comparison corrections):

summary(contra_all_N170_hemi[1:4], adjust = "none")

##  Duration_pairwise Hemisphere_pairwise Category Type        estimate        SE    df   lower.CL   upper.CL t.ratio p.value
##  17 - 200          Left - Right        face     intact    -0.6190389 0.2636081 45.58 -1.1497851 -0.0882927  -2.348  0.0233
##  17 - 200          Left - Right        house    intact    -0.3034367 0.2639238 45.80 -0.8347496  0.2278762  -1.150  0.2562
##  17 - 200          Left - Right        face     scrambled  0.1003766 0.2708823 50.83 -0.4434872  0.6442404   0.371  0.7125
##  17 - 200          Left - Right        house    scrambled -0.1159567 0.2708231 50.78 -0.6597133  0.4277999  -0.428  0.6703
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95

summary(contra_all_N170_hemi[1:4], delta = delta_null, level = .9)

##  Duration_pairwise Hemisphere_pairwise Category Type        estimate        SE    df   lower.CL   upper.CL t.ratio p.value
##  17 - 200          Left - Right        face     intact    -0.6190389 0.2636081 45.58 -1.0616303 -0.1764475   0.452  0.6731
##  17 - 200          Left - Right        house    intact    -0.3034367 0.2639238 45.80 -0.7465144  0.1396410  -0.745  0.2301
##  17 - 200          Left - Right        face     scrambled  0.1003766 0.2708823 50.83 -0.3534570  0.5542102  -1.475  0.0732
##  17 - 200          Left - Right        house    scrambled -0.1159567 0.2708231 50.78 -0.5696985  0.3377851  -1.418  0.0811
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

Comparisons of face-specific N170 amplitudes between Durations (without multiple comparison corrections):

summary(contra_all_N170_spec[1:4], adjust = "none")

##  Category_pairwise Duration_pairwise Hemisphere Type        estimate        SE     df   lower.CL  upper.CL t.ratio p.value
##  face - house      17 - 200          Left       intact     1.9226184 0.2151955 104.11  1.4958828 2.3493541   8.934  <.0001
##  face - house      17 - 200          Right      intact     2.2382207 0.2151955 104.11  1.8114850 2.6649563  10.401  <.0001
##  face - house      17 - 200          Left       scrambled  0.1528882 0.2238302 121.85 -0.2902115 0.5959880   0.683  0.4959
##  face - house      17 - 200          Right      scrambled -0.0634451 0.2238302 121.85 -0.5065448 0.3796547  -0.283  0.7773
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95

summary(contra_all_N170_spec[1:4], delta = delta_null, level = .9, adjust = "none")

##  Category_pairwise Duration_pairwise Hemisphere Type        estimate        SE     df   lower.CL  upper.CL t.ratio p.value
##  face - house      17 - 200          Left       intact     1.9226184 0.2151955 104.11  1.5654752 2.2797617   6.611  1.0000
##  face - house      17 - 200          Right      intact     2.2382207 0.2151955 104.11  1.8810774 2.5953639   8.077  1.0000
##  face - house      17 - 200          Left       scrambled  0.1528882 0.2238302 121.85 -0.2181003 0.5238768  -1.551  0.0618
##  face - house      17 - 200          Right      scrambled -0.0634451 0.2238302 121.85 -0.4344336 0.3075435  -1.950  0.0267
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

5.3 Linear mixed model with subjective confidence

5.3.1 Converting response to subjective confidence

df.erp.SC.E2 <- {
  df.erp.E2 %>% 
    filter(Type == "intact") %>%  # only keep the intact trials
    mutate(Confidence = if_else(Response %in% c("11", "55"), "high", 
                                if_else(Response %in% c("12", "54"), "low",
                                        if_else(substring(Response, 2) == "3", "guess", "NA"))),
           DuraConf = paste(Duration, Confidence, sep = "_")) %>% 
    filter(Confidence != "NA",
           !(DuraConf %in% c("200_guess", "200_low"))) %>% 
    mutate(Confidence = as.factor(Confidence),
           DuraConf = as.factor(DuraConf))
}

5.3.2 Set the successive difference coding

# successive difference coding
df.erp.SC.E2 <- sdif_coding_E205_erp(df.erp.SC.E2)

5.3.3 Amplitudes of the P1

# only keep the data for amplitudes of the P1 (correct and incorrect erps)
df.erp.P1.SC.E2 <- {
  df.erp.SC.E2 %>% 
    filter(Component == "P1") 
}

if (saveCSV) {
  output.erp.P1.SC.E2 <- file.path(folder_nesi, "E205_erp_P1_SC.RData")
  save(df.erp.P1.SC.E2, file = output.erp.P1.SC.E2)
}

# the structure of this dataset
head(df.erp.P1.SC.E2, 10)

## # A tibble: 10 x 78
##    Channel Event Urevent TrialNumber Block_ST_table Response urResponse    RT  urRT Reject Rejthresh Rejjp SubjCode ChanCent ChanCluster_1 ChanCluster_2 ChanCluster_3 ChanCluster_4 ChanCluster_5 ChanCluster_6 ChanCluster_7 Hemisphere Component MeanAmp MethodCode Method        IsPositive StartFrame PeakFrame EndFrame StartTime PeakTime EndTime FrameSize WindowSize StartAmplitude PeakAmplitude EndAmplitude Session Block_beha_table Trial Resp_ACC Resp_OnsetDelay Resp_RT stimName   Type   Category Duration Stimuli Confidence DuraConf Type_C Cate_C Dura_C Type_Cate Type_Dura Cate_Dura Type_Cate_Dura Hemi_C Type_Hemi Cate_Hemi Dura_Hemi Type_Cate_Hemi Type_Dura_Hemi Cate_Dura_Hemi Type_Cate_Dura_Hemi ConLG_C ConHL_C Con2H_C Cate_ConLH Hemi_ConLH Cate_Hemi_ConLH Cate_ConGL Hemi_ConGL Cate_Hemi_ConGL Cate_Con2G Hemi_Con2G Cate_Hemi_Con2G
##    <chr>   <chr>   <dbl>       <dbl>          <dbl>    <dbl> <fct>      <dbl> <dbl>  <dbl>     <dbl> <dbl> <fct>    <chr>    <chr>         <chr>         <chr>         <chr>         <chr>         <chr>         <chr>         <fct>      <chr>       <dbl>      <dbl> <chr>              <dbl>      <dbl>     <dbl>    <dbl>     <dbl>    <dbl>   <dbl>     <dbl>      <dbl>          <dbl>         <dbl>        <dbl>   <dbl>            <dbl> <dbl>    <dbl>           <dbl>   <dbl> <chr>      <fct>  <fct>    <fct>    <chr>   <fct>      <fct>     <dbl>  <dbl>  <dbl>     <dbl>     <dbl>     <dbl>          <dbl>  <dbl>     <dbl>     <dbl>     <dbl>          <dbl>          <dbl>          <dbl>               <dbl>   <dbl>   <dbl>   <dbl>      <dbl>      <dbl>           <dbl>      <dbl>      <dbl>           <dbl>      <dbl>      <dbl>           <dbl>
##  1 E65     NF2+     2590         646              2       11 RE11         706   756      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       P1         -1.08           2 MeanAmplitude          1         24        29       34        96      116     136        10         40         -5.91           2.99       -4.57        1               35    11        1              16     540 NfF040.bmp intact face     200      fF040   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.125     -0.125          0.0625      -0.25      -0.25           0.125      0.125      0.125         -0.0625
##  2 E65     NF2+     2610         651              2       11 RE11         564   614      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       P1          2.91           2 MeanAmplitude          1         24        34       34        96      136     136        10         40          1.44           7.07        7.07        1               35    16        1              17     397 NfF078.bmp intact face     200      fF078   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.125     -0.125          0.0625      -0.25      -0.25           0.125      0.125      0.125         -0.0625
##  3 E65     NF2+     2622         654              2       11 RE11         574   624      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       P1          2.35           2 MeanAmplitude          1         24        27       34        96      108     136        10         40         -1.17           5.15        3.13        1               35    19        1              16     408 NfM122.bmp intact face     200      fM122   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.125     -0.125          0.0625      -0.25      -0.25           0.125      0.125      0.125         -0.0625
##  4 E65     NF2+     2626         655              2       11 RE11         387   437      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       P1         10.7            2 MeanAmplitude          1         24        25       34        96      100     136        10         40         11.5           12.1         9.94        1               35    20        1              17     220 NfM013.bmp intact face     200      fM013   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.125     -0.125          0.0625      -0.25      -0.25           0.125      0.125      0.125         -0.0625
##  5 E65     NF2+     2666         665              2       11 RE11         361   411      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       P1          0.608          2 MeanAmplitude          1         24        28       34        96      112     136        10         40         -5.60           4.06        0.541       1               35    30        1              16     195 NfM142.bmp intact face     200      fM142   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.125     -0.125          0.0625      -0.25      -0.25           0.125      0.125      0.125         -0.0625
##  6 E65     NF2+     2686         670              2       11 RE11         366   416      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       P1         -1.54           2 MeanAmplitude          1         24        29       34        96      116     136        10         40         -6.76           1.08       -1.13        1               35    35        1              16     200 NfF112.bmp intact face     200      fF112   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.125     -0.125          0.0625      -0.25      -0.25           0.125      0.125      0.125         -0.0625
##  7 E65     NF2+     2690         671              2       11 RE11         423   473      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       P1          3.35           2 MeanAmplitude          1         24        30       34        96      120     136        10         40          1.09           7.85       -0.560       1               35    36        1              17     256 NfF017.bmp intact face     200      fF017   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.125     -0.125          0.0625      -0.25      -0.25           0.125      0.125      0.125         -0.0625
##  8 E65     NF2+     2722         679              2       11 RE11         446   496      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       P1          6.01           2 MeanAmplitude          1         24        34       34        96      136     136        10         40          0.630          9.21        9.21        1               35    44        1              17     279 NfF068.bmp intact face     200      fF068   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.125     -0.125          0.0625      -0.25      -0.25           0.125      0.125      0.125         -0.0625
##  9 E65     NF2+     2778         693              2       11 RE11         454   504      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       P1          2.87           2 MeanAmplitude          1         24        31       34        96      124     136        10         40         -2.85           6.41        1.56        1               35    58        1              16     288 NfF076.bmp intact face     200      fF076   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.125     -0.125          0.0625      -0.25      -0.25           0.125      0.125      0.125         -0.0625
## 10 E65     NF2+     2790         696              2       11 RE11         441   491      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       P1          0.793          2 MeanAmplitude          1         24        28       34        96      112     136        10         40          0.139          2.21        0.914       1               35    61        1              16     275 NfF079.bmp intact face     200      fF079   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.125     -0.125          0.0625      -0.25      -0.25           0.125      0.125      0.125         -0.0625

5.3.3.1 The maximal model

lmm.max.P1.SC.E2 <- lmer(MeanAmp ~ Category * Hemisphere * DuraConf +
                           (1 + Cate_C + Hemi_C + Cate_Hemi | SubjCode) +
                           (1 + Hemi_C | Stimuli),
                         data = df.erp.P1.SC.E2,
                         verbose = TRUE,
                         control = lmerControl(optimizer = "bobyqa"))  # nloptwrap Nelder_Mead

## start par. =  1 0 1 1 0 0 0 1 0 0 1 0 1 fn =  221091.6 
## At return
## eval: 1576 fn:      220102.61 par: 0.0378072 0.00819983  0.00000 0.338144 -0.0678822 0.0508083 -0.000956374 0.0773424 0.0621299 0.0254038 0.214512 -0.0111326 4.18013e-06

print(summary(lmm.max.P1.SC.E2), corr = FALSE)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category * Hemisphere * DuraConf + (1 + Cate_C + Hemi_C +      Cate_Hemi | SubjCode) + (1 + Hemi_C | Stimuli)
##    Data: df.erp.P1.SC.E2
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 220102.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.6020 -0.5787 -0.0155  0.5775  8.4272 
## 
## Random effects:
##  Groups   Name        Variance  Std.Dev. Corr             
##  Stimuli  (Intercept)  0.021529 0.14673                   
##           Hemi_C       0.001013 0.03182  1.00             
##  SubjCode (Intercept)  1.722143 1.31230                   
##           Cate_C       0.159498 0.39937  -0.66            
##           Hemi_C       0.790078 0.88886   0.22  0.06      
##           Cate_Hemi    0.011600 0.10770  -0.03  0.71 -0.14
##  Residual             15.061410 3.88090                   
## Number of obs: 39600, groups:  Stimuli, 80; SubjCode, 28
## 
## Fixed effects:
##                                         Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)                            1.248e+00  2.495e-01  2.730e+01   5.002 2.94e-05 ***
## Category2-1                            5.822e-02  9.297e-02  3.683e+01   0.626   0.5350    
## Hemisphere2-1                          5.166e-02  1.733e-01  2.748e+01   0.298   0.7678    
## DuraConf2-1                            1.051e-01  6.125e-02  2.983e+04   1.716   0.0862 .  
## DuraConf3-2                            2.647e-01  6.564e-02  1.776e+04   4.033 5.54e-05 ***
## DuraConf4-3                            3.971e-02  6.696e-02  2.288e+04   0.593   0.5532    
## Category2-1:Hemisphere2-1             -9.480e-02  8.859e-02  2.180e+02  -1.070   0.2857    
## Category2-1:DuraConf2-1               -2.347e-01  1.209e-01  1.212e+04  -1.941   0.0523 .  
## Category2-1:DuraConf3-2                3.841e-02  1.263e-01  2.598e+03   0.304   0.7612    
## Category2-1:DuraConf4-3                5.656e-02  1.312e-01  6.594e+03   0.431   0.6663    
## Hemisphere2-1:DuraConf2-1              1.840e-02  1.205e-01  3.585e+04   0.153   0.8786    
## Hemisphere2-1:DuraConf3-2              7.275e-02  1.272e-01  2.436e+04   0.572   0.5673    
## Hemisphere2-1:DuraConf4-3              5.993e-02  1.314e-01  2.522e+04   0.456   0.6483    
## Category2-1:Hemisphere2-1:DuraConf2-1  1.322e-01  2.334e-01  2.924e+04   0.566   0.5713    
## Category2-1:Hemisphere2-1:DuraConf3-2 -1.058e-01  2.330e-01  1.267e+04  -0.454   0.6498    
## Category2-1:Hemisphere2-1:DuraConf4-3 -1.807e-01  2.513e-01  2.114e+04  -0.719   0.4721    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular

5.3.3.2 The zero-correlation-parameter model

lmm.zcp.P1.SC.E2 <- update(lmm.max.P1.SC.E2,
                           formula = MeanAmp ~ Category * Hemisphere * DuraConf +
                             (1 + Cate_C + Hemi_C + Cate_Hemi || SubjCode) +
                             (1 + Hemi_C || Stimuli))

## start par. =  1 1 1 1 1 1 fn =  221091.6 
## At return
## eval: 420 fn:      220118.27 par: 2.50536e-07 0.0375916 3.48909e-06 0.229004 0.102779 0.338215

print(summary(lmm.zcp.P1.SC.E2), corr = FALSE)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category + Hemisphere + DuraConf + (1 + Cate_C + Hemi_C +      Cate_Hemi || SubjCode) + (1 + Hemi_C || Stimuli) + Category:Hemisphere +      Category:DuraConf + Hemisphere:DuraConf + Category:Hemisphere:DuraConf
##    Data: df.erp.P1.SC.E2
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 220118.3
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
## -6.604 -0.578 -0.015  0.578  8.456 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev.
##  Stimuli    Hemi_C       0.00000 0.0000  
##  Stimuli.1  (Intercept)  0.02129 0.1459  
##  SubjCode   Cate_Hemi    0.00000 0.0000  
##  SubjCode.1 Hemi_C       0.78993 0.8888  
##  SubjCode.2 Cate_C       0.15911 0.3989  
##  SubjCode.3 (Intercept)  1.72300 1.3126  
##  Residual               15.06265 3.8811  
## Number of obs: 39600, groups:  Stimuli, 80; SubjCode, 28
## 
## Fixed effects:
##                                         Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)                            1.249e+00  2.495e-01  2.730e+01   5.007 2.91e-05 ***
## Category2-1                            6.073e-02  9.286e-02  3.677e+01   0.654   0.5172    
## Hemisphere2-1                          5.095e-02  1.732e-01  2.745e+01   0.294   0.7709    
## DuraConf2-1                            1.039e-01  6.148e-02  3.568e+04   1.690   0.0911 .  
## DuraConf3-2                            2.724e-01  6.610e-02  2.887e+04   4.121 3.78e-05 ***
## DuraConf4-3                            3.293e-02  6.739e-02  3.365e+04   0.489   0.6251    
## Category2-1:Hemisphere2-1             -9.713e-02  8.593e-02  3.917e+04  -1.130   0.2584    
## Category2-1:DuraConf2-1               -2.413e-01  1.220e-01  2.111e+04  -1.979   0.0479 *  
## Category2-1:DuraConf3-2                6.113e-02  1.290e-01  5.815e+03   0.474   0.6357    
## Category2-1:DuraConf4-3                4.158e-02  1.330e-01  1.365e+04   0.313   0.7546    
## Hemisphere2-1:DuraConf2-1              2.186e-02  1.205e-01  3.789e+04   0.181   0.8561    
## Hemisphere2-1:DuraConf3-2              6.451e-02  1.272e-01  2.990e+04   0.507   0.6121    
## Hemisphere2-1:DuraConf4-3              6.541e-02  1.315e-01  3.598e+04   0.497   0.6189    
## Category2-1:Hemisphere2-1:DuraConf2-1  1.270e-01  2.332e-01  3.936e+04   0.545   0.5860    
## Category2-1:Hemisphere2-1:DuraConf3-2 -1.051e-01  2.325e-01  3.908e+04  -0.452   0.6513    
## Category2-1:Hemisphere2-1:DuraConf4-3 -1.768e-01  2.512e-01  3.933e+04  -0.704   0.4814    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular

5.3.3.3 The reduced model

summary(rePCA(lmm.zcp.P1.SC.E2))

## $Stimuli
## Importance of components:
##                           [,1] [,2]
## Standard deviation     0.03759    0
## Proportion of Variance 1.00000    0
## Cumulative Proportion  1.00000    1
## 
## $SubjCode
## Importance of components:
##                          [,1]   [,2]    [,3] [,4]
## Standard deviation     0.3382 0.2290 0.10278    0
## Proportion of Variance 0.6448 0.2956 0.05955    0
## Cumulative Proportion  0.6448 0.9405 1.00000    1

lmm.rdc.P1.SC.E2 <- update(lmm.zcp.P1.SC.E2,
                           formula = MeanAmp ~ Category * Hemisphere * DuraConf +
                             (1 + Cate_C + Hemi_C || SubjCode) +
                             (1 | Stimuli))

## start par. =  1 1 1 1 fn =  220659.7 
## At return
## eval: 217 fn:      220118.27 par: 0.0375915 0.229001 0.102778 0.338215

print(summary(lmm.rdc.P1.SC.E2), corr = FALSE)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category + Hemisphere + DuraConf + (1 + Cate_C + Hemi_C ||      SubjCode) + (1 | Stimuli) + Category:Hemisphere + Category:DuraConf +      Hemisphere:DuraConf + Category:Hemisphere:DuraConf
##    Data: df.erp.P1.SC.E2
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 220118.3
## 
## Scaled residuals: 
##    Min     1Q Median     3Q    Max 
## -6.604 -0.578 -0.015  0.578  8.456 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev.
##  Stimuli    (Intercept)  0.02129 0.1459  
##  SubjCode   Hemi_C       0.78991 0.8888  
##  SubjCode.1 Cate_C       0.15911 0.3989  
##  SubjCode.2 (Intercept)  1.72300 1.3126  
##  Residual               15.06265 3.8811  
## Number of obs: 39600, groups:  Stimuli, 80; SubjCode, 28
## 
## Fixed effects:
##                                         Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)                            1.249e+00  2.495e-01  2.730e+01   5.007 2.91e-05 ***
## Category2-1                            6.073e-02  9.286e-02  3.677e+01   0.654   0.5172    
## Hemisphere2-1                          5.095e-02  1.732e-01  2.746e+01   0.294   0.7709    
## DuraConf2-1                            1.039e-01  6.148e-02  3.568e+04   1.690   0.0911 .  
## DuraConf3-2                            2.724e-01  6.610e-02  2.887e+04   4.121 3.78e-05 ***
## DuraConf4-3                            3.293e-02  6.739e-02  3.365e+04   0.489   0.6251    
## Category2-1:Hemisphere2-1             -9.713e-02  8.593e-02  3.917e+04  -1.130   0.2584    
## Category2-1:DuraConf2-1               -2.413e-01  1.220e-01  2.111e+04  -1.979   0.0479 *  
## Category2-1:DuraConf3-2                6.113e-02  1.290e-01  5.815e+03   0.474   0.6357    
## Category2-1:DuraConf4-3                4.158e-02  1.330e-01  1.365e+04   0.313   0.7546    
## Hemisphere2-1:DuraConf2-1              2.186e-02  1.205e-01  3.789e+04   0.181   0.8561    
## Hemisphere2-1:DuraConf3-2              6.451e-02  1.272e-01  2.990e+04   0.507   0.6121    
## Hemisphere2-1:DuraConf4-3              6.541e-02  1.315e-01  3.598e+04   0.497   0.6189    
## Category2-1:Hemisphere2-1:DuraConf2-1  1.270e-01  2.332e-01  3.936e+04   0.545   0.5860    
## Category2-1:Hemisphere2-1:DuraConf3-2 -1.051e-01  2.325e-01  3.908e+04  -0.452   0.6513    
## Category2-1:Hemisphere2-1:DuraConf4-3 -1.768e-01  2.512e-01  3.933e+04  -0.704   0.4814    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

anova(lmm.rdc.P1.SC.E2, lmm.zcp.P1.SC.E2, refit = FALSE)

## Data: df.erp.P1.SC.E2
## Models:
## lmm.rdc.P1.SC.E2: MeanAmp ~ Category + Hemisphere + DuraConf + (1 + Cate_C + Hemi_C || 
## lmm.rdc.P1.SC.E2:     SubjCode) + (1 | Stimuli) + Category:Hemisphere + Category:DuraConf + 
## lmm.rdc.P1.SC.E2:     Hemisphere:DuraConf + Category:Hemisphere:DuraConf
## lmm.zcp.P1.SC.E2: MeanAmp ~ Category + Hemisphere + DuraConf + (1 + Cate_C + Hemi_C + 
## lmm.zcp.P1.SC.E2:     Cate_Hemi || SubjCode) + (1 + Hemi_C || Stimuli) + Category:Hemisphere + 
## lmm.zcp.P1.SC.E2:     Category:DuraConf + Hemisphere:DuraConf + Category:Hemisphere:DuraConf
##                  npar    AIC    BIC  logLik deviance Chisq Df Pr(>Chisq)
## lmm.rdc.P1.SC.E2   21 220160 220341 -110059   220118                    
## lmm.zcp.P1.SC.E2   23 220164 220362 -110059   220118     0  2          1

5.3.3.4 The extended model

lmm.etd.P1.SC.E2 <- update(lmm.rdc.P1.SC.E2,
                           formula = MeanAmp ~ Category * Hemisphere * DuraConf +
                             (1 + Cate_C + Hemi_C | SubjCode) +
                             (1 | Stimuli))

## start par. =  1 1 0 0 1 0 1 fn =  220659.7 
## At return
## eval: 554 fn:      220104.36 par: 0.0376817 0.338067 -0.0679950 0.0508027 0.0772712 0.0621654 0.214439

print(summary(lmm.etd.P1.SC.E2), corr = FALSE)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category + Hemisphere + DuraConf + (1 + Cate_C + Hemi_C |      SubjCode) + (1 | Stimuli) + Category:Hemisphere + Category:DuraConf +      Hemisphere:DuraConf + Category:Hemisphere:DuraConf
##    Data: df.erp.P1.SC.E2
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 220104.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.6075 -0.5789 -0.0158  0.5770  8.4361 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr       
##  Stimuli  (Intercept)  0.02139 0.1462              
##  SubjCode (Intercept)  1.72149 1.3121              
##           Cate_C       0.15958 0.3995   -0.66      
##           Hemi_C       0.78972 0.8887    0.22  0.06
##  Residual             15.06256 3.8811              
## Number of obs: 39600, groups:  Stimuli, 80; SubjCode, 28
## 
## Fixed effects:
##                                         Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)                            1.247e+00  2.494e-01  2.730e+01   5.001 2.95e-05 ***
## Category2-1                            5.783e-02  9.295e-02  3.674e+01   0.622   0.5377    
## Hemisphere2-1                          5.142e-02  1.732e-01  2.745e+01   0.297   0.7688    
## DuraConf2-1                            1.059e-01  6.127e-02  3.012e+04   1.728   0.0840 .  
## DuraConf3-2                            2.622e-01  6.568e-02  1.817e+04   3.992 6.58e-05 ***
## DuraConf4-3                            4.173e-02  6.698e-02  2.308e+04   0.623   0.5333    
## Category2-1:Hemisphere2-1             -9.602e-02  8.592e-02  3.911e+04  -1.118   0.2638    
## Category2-1:DuraConf2-1               -2.375e-01  1.210e-01  1.240e+04  -1.962   0.0497 *  
## Category2-1:DuraConf3-2                3.344e-02  1.265e-01  2.649e+03   0.264   0.7916    
## Category2-1:DuraConf4-3                6.136e-02  1.313e-01  6.668e+03   0.467   0.6402    
## Hemisphere2-1:DuraConf2-1              2.104e-02  1.205e-01  3.756e+04   0.175   0.8614    
## Hemisphere2-1:DuraConf3-2              6.857e-02  1.271e-01  2.857e+04   0.539   0.5896    
## Hemisphere2-1:DuraConf4-3              6.261e-02  1.314e-01  3.537e+04   0.476   0.6338    
## Category2-1:Hemisphere2-1:DuraConf2-1  1.247e-01  2.332e-01  3.933e+04   0.535   0.5928    
## Category2-1:Hemisphere2-1:DuraConf3-2 -9.808e-02  2.324e-01  3.898e+04  -0.422   0.6731    
## Category2-1:Hemisphere2-1:DuraConf4-3 -1.824e-01  2.511e-01  3.930e+04  -0.726   0.4677    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

anova(lmm.rdc.P1.SC.E2, lmm.etd.P1.SC.E2, refit = FALSE)

## Data: df.erp.P1.SC.E2
## Models:
## lmm.rdc.P1.SC.E2: MeanAmp ~ Category + Hemisphere + DuraConf + (1 + Cate_C + Hemi_C || 
## lmm.rdc.P1.SC.E2:     SubjCode) + (1 | Stimuli) + Category:Hemisphere + Category:DuraConf + 
## lmm.rdc.P1.SC.E2:     Hemisphere:DuraConf + Category:Hemisphere:DuraConf
## lmm.etd.P1.SC.E2: MeanAmp ~ Category + Hemisphere + DuraConf + (1 + Cate_C + Hemi_C | 
## lmm.etd.P1.SC.E2:     SubjCode) + (1 | Stimuli) + Category:Hemisphere + Category:DuraConf + 
## lmm.etd.P1.SC.E2:     Hemisphere:DuraConf + Category:Hemisphere:DuraConf
##                  npar    AIC    BIC  logLik deviance  Chisq Df Pr(>Chisq)   
## lmm.rdc.P1.SC.E2   21 220160 220341 -110059   220118                        
## lmm.etd.P1.SC.E2   24 220152 220358 -110052   220104 13.905  3   0.003037 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.3.5 The optimal model

lmm.opt.P1.SC.E205 <- lmm.etd.P1.SC.E2

# print(summary(lmm.opt.P1.SC.E2), corr = FALSE)
summary(lmm.opt.P1.SC.E205)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category + Hemisphere + DuraConf + (1 + Cate_C + Hemi_C |      SubjCode) + (1 | Stimuli) + Category:Hemisphere + Category:DuraConf +      Hemisphere:DuraConf + Category:Hemisphere:DuraConf
##    Data: df.erp.P1.SC.E2
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 220104.4
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.6075 -0.5789 -0.0158  0.5770  8.4361 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr       
##  Stimuli  (Intercept)  0.02139 0.1462              
##  SubjCode (Intercept)  1.72149 1.3121              
##           Cate_C       0.15958 0.3995   -0.66      
##           Hemi_C       0.78972 0.8887    0.22  0.06
##  Residual             15.06256 3.8811              
## Number of obs: 39600, groups:  Stimuli, 80; SubjCode, 28
## 
## Fixed effects:
##                                         Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)                            1.247e+00  2.494e-01  2.730e+01   5.001 2.95e-05 ***
## Category2-1                            5.783e-02  9.295e-02  3.674e+01   0.622   0.5377    
## Hemisphere2-1                          5.142e-02  1.732e-01  2.745e+01   0.297   0.7688    
## DuraConf2-1                            1.059e-01  6.127e-02  3.012e+04   1.728   0.0840 .  
## DuraConf3-2                            2.622e-01  6.568e-02  1.817e+04   3.992 6.58e-05 ***
## DuraConf4-3                            4.173e-02  6.698e-02  2.308e+04   0.623   0.5333    
## Category2-1:Hemisphere2-1             -9.602e-02  8.592e-02  3.911e+04  -1.118   0.2638    
## Category2-1:DuraConf2-1               -2.375e-01  1.210e-01  1.240e+04  -1.962   0.0497 *  
## Category2-1:DuraConf3-2                3.344e-02  1.265e-01  2.649e+03   0.264   0.7916    
## Category2-1:DuraConf4-3                6.136e-02  1.313e-01  6.668e+03   0.467   0.6402    
## Hemisphere2-1:DuraConf2-1              2.104e-02  1.205e-01  3.756e+04   0.175   0.8614    
## Hemisphere2-1:DuraConf3-2              6.857e-02  1.271e-01  2.857e+04   0.539   0.5896    
## Hemisphere2-1:DuraConf4-3              6.261e-02  1.314e-01  3.537e+04   0.476   0.6338    
## Category2-1:Hemisphere2-1:DuraConf2-1  1.247e-01  2.332e-01  3.933e+04   0.535   0.5928    
## Category2-1:Hemisphere2-1:DuraConf3-2 -9.808e-02  2.324e-01  3.898e+04  -0.422   0.6731    
## Category2-1:Hemisphere2-1:DuraConf4-3 -1.824e-01  2.511e-01  3.930e+04  -0.726   0.4677    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

confint(lmm.opt.P1.SC.E205, method = "Wald")

##                                             2.5 %        97.5 %
## .sig01                                         NA            NA
## .sig02                                         NA            NA
## .sig03                                         NA            NA
## .sig04                                         NA            NA
## .sig05                                         NA            NA
## .sig06                                         NA            NA
## .sig07                                         NA            NA
## .sigma                                         NA            NA
## (Intercept)                            0.75855980  1.7362675434
## Category2-1                           -0.12434173  0.2399972799
## Hemisphere2-1                         -0.28803290  0.3908802709
## DuraConf2-1                           -0.01421219  0.2259623763
## DuraConf3-2                            0.13345670  0.3909005166
## DuraConf4-3                           -0.08955719  0.1730168930
## Category2-1:Hemisphere2-1             -0.26442372  0.0723834042
## Category2-1:DuraConf2-1               -0.47465648 -0.0003054915
## Category2-1:DuraConf3-2               -0.21453830  0.2814169651
## Category2-1:DuraConf4-3               -0.19592560  0.3186522135
## Hemisphere2-1:DuraConf2-1             -0.21510192  0.2571773951
## Hemisphere2-1:DuraConf3-2             -0.18060392  0.3177448927
## Hemisphere2-1:DuraConf4-3             -0.19499840  0.3202178645
## Category2-1:Hemisphere2-1:DuraConf2-1 -0.33231528  0.5816902531
## Category2-1:Hemisphere2-1:DuraConf3-2 -0.55367803  0.3575267455
## Category2-1:Hemisphere2-1:DuraConf4-3 -0.67462460  0.3098546508

anova(lmm.opt.P1.SC.E205)

## Type III Analysis of Variance Table with Satterthwaite's method
##                              Sum Sq Mean Sq NumDF DenDF F value    Pr(>F)    
## Category                       5.83   5.831     1    37  0.3871    0.5377    
## Hemisphere                     1.33   1.328     1    27  0.0882    0.7688    
## DuraConf                     792.41 264.138     3 27336 17.5360 2.277e-11 ***
## Category:Hemisphere           18.81  18.811     1 39110  1.2489    0.2638    
## Category:DuraConf             61.11  20.370     3  6520  1.3524    0.2555    
## Hemisphere:DuraConf           27.59   9.197     3 34874  0.6106    0.6080    
## Category:Hemisphere:DuraConf  23.10   7.701     3 39292  0.5113    0.6745    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.3.6 Diagnostic plots

qqplot_lmer(lmm.opt.P1.SC.E205)

5.3.3.7 Estimated marginal means

emm.P1.SC.E205 <- emmeans(lmm.opt.P1.SC.E205, ~ Category + DuraConf + Hemisphere)

emm.P1.SC.E205 %>% 
  as_tibble() %>% 
  arrange(Category, DuraConf)

## # A tibble: 16 x 8
##    Category DuraConf Hemisphere emmean    SE    df lower.CL upper.CL
##    <fct>    <fct>    <fct>       <dbl> <dbl> <dbl>    <dbl>    <dbl>
##  1 face     17_guess Left        0.908 0.295  36.6    0.311     1.51
##  2 face     17_guess Right       0.941 0.323  34.6    0.284     1.60
##  3 face     17_low   Left        1.15  0.283  31.2    0.576     1.73
##  4 face     17_low   Right       1.15  0.313  30.4    0.506     1.78
##  5 face     17_high  Left        1.34  0.280  29.9    0.768     1.91
##  6 face     17_high  Right       1.45  0.310  29.3    0.815     2.08
##  7 face     200_high Left        1.27  0.285  32.2    0.693     1.86
##  8 face     200_high Right       1.54  0.315  31.2    0.895     2.18
##  9 house    17_guess Left        1.16  0.232  32.2    0.687     1.63
## 10 house    17_guess Right       1.10  0.270  30.8    0.546     1.65
## 11 house    17_low   Left        1.10  0.231  31.8    0.634     1.58
## 12 house    17_low   Right       1.13  0.269  30.4    0.577     1.68
## 13 house    17_high  Left        1.37  0.251  43.9    0.869     1.88
## 14 house    17_high  Right       1.42  0.287  39.0    0.835     2.00
## 15 house    200_high Left        1.46  0.237  35.2    0.980     1.94
## 16 house    200_high Right       1.47  0.274  32.8    0.915     2.03

5.3.3.8 Plots

# emmip(emm.P1.SC.E205, Category ~ DuraConf | Hemisphere, CIs = TRUE)

duraconf.labels = c("17ms\nguess", "17ms\nlow", "17ms\nhigh", "200ms\nhigh")

p1.conf.LinePlot = {
  ggplot(data = data.frame(emm.P1.SC.E205), aes(y = emmean, x = DuraConf, color = Category, group = Category)) +  # set the data, varialbes for x and y axises, and the variable for dividing data
    geom_point(size = 2) +
    geom_line(aes(linetype = Category), size = .85) +  # position = "dodge", alpha = .7
    scale_linetype_manual(values=c("solid", "dashed")) +
    geom_errorbar(mapping = aes(ymin = lower.CL, ymax = upper.CL), linetype = 1,  # set the error bar
                  show.legend = FALSE, width = 0.25, alpha = .5) + # ,                   position = position_dodge(width=0.9)
    facet_grid(. ~ Hemisphere, # switch = "x",
               labeller = labeller(Hemisphere = hemiLabel)) +
    scale_y_reverse(limits = ylimit_p1) +  # set the limit for y axis
    scale_x_discrete(labels = duraconf.labels) +
    # scale_y_continuous(expand= c(0, 0)) +  # remove the space between columns and x axis
    labs(x = "Duration and Subjective Confidence", y = expression(paste("Amplitudes (", mu, "V)")), fill = "Category") +  # set the names for main, x and y axises title = "", 
    # scale_x_discrete(labels = c("left", "right")) +
    # geom_text(label = c("", "", "", "", "***", "", "", ""), color = "red", size = 6, nudge_y = 0.5, nudge_x = 0.5) + # add starts to the significant columns
    theme_bw() +
    erp_theme
}

# p1.conf.LinePlot.slide <- {
#   p1.conf.LinePlot + 
#     theme(legend.position="right",
#           axis.line = element_line(color="black", size = 3))
# }
# ggsave('P1_conf.png', p1.conf.LinePlot.slide, width = 8.5, height = 4)

p1.conf.LinePlot

duration_color <- c(brewer.pal(9, 'Blues')[c(6, 7, 9)], brewer.pal(12, "Paired")[6]) 
category_color <- brewer.pal(8, 'Dark2')[c(6,1)]

p1_con_LinePlot = {
  ggplot(data = data.frame(emm.P1.SC.E205), aes(y = emmean, x = DuraConf, group = Category)) +  # set the data, varialbes for x and y axises, and the variable for dividing data
    geom_point(aes(fill = DuraConf), size=4, shape=21, stroke=0) +
    scale_fill_manual(values = duration_color, guide = FALSE) +
    geom_line(aes(linetype = Category, color = Category), size = .85) +  # position = "dodge", alpha = .7
    scale_color_manual(values=category_color) +
    scale_linetype_manual(values=c("solid", "dashed")) +
    geom_errorbar(mapping = aes(ymin = lower.CL, ymax = upper.CL), linetype = 1,  # set the error bar
                  show.legend = FALSE, width = 0.25, alpha = .5) + # ,                   position = position_dodge(width=0.9)
    facet_grid(. ~ Hemisphere, # switch = "x",
               labeller = labeller(Hemisphere = hemiLabel)) +
    scale_y_reverse(limits = ylimit_p1) +  # set the limit for y axis
    scale_x_discrete(labels = duraconf.labels) +
    # scale_y_continuous(expand= c(0, 0)) +  # remove the space between columns and x axis
    labs(x = "Duration and Subjective Confidence", y = expression(paste("P1 Amplitudes (", mu, "V)")), fill = "Category") +  # set the names for main, x and y axises title = "", 
    theme_bw() +
    erp_theme + 
    theme(
      axis.title.x = element_text(margin = margin(t = 10, r = 0, b = 0, l = 0)),
      legend.margin = margin(t = -10),
      legend.title=element_text(size=12), 
      legend.text=element_text(size=12),
    )
}

ggsave('P1_con_pub.pdf', p1_con_LinePlot, width = 7, height = 5)

5.3.3.9 Contrast

5.3.3.9.1 Main results

Comparisons of P1 amplitudes for (intact) faces with different durations and confidence (left hemisphere):

contra_SC_P1 <- contrast(emmeans(lmm.opt.P1.SC.E205, ~ DuraConf | Hemisphere + Category), # 
                             "pairwise", adjust = "Bonferroni", infer = TRUE)

contra_SC_P1[1:6]

##  contrast            Hemisphere Category   estimate        SE       df   lower.CL   upper.CL t.ratio p.value
##  17_guess - 17_low   Left       face     -0.2452686 0.1354348 37115.10 -0.6025995  0.1120623  -1.811  0.4209
##  17_guess - 17_high  Left       face     -0.4319234 0.1376417 20687.49 -0.7950925 -0.0687543  -3.138  0.0102
##  17_guess - 200_high Left       face     -0.3660705 0.1425063 36491.93 -0.7420593  0.0099183  -2.569  0.0613
##  17_low - 17_high    Left       face     -0.1866548 0.1094711 17691.79 -0.4755002  0.1021907  -1.705  0.5292
##  17_low - 200_high   Left       face     -0.1208019 0.1171315 37690.70 -0.4298413  0.1882376  -1.031  1.0000
##  17_high - 200_high  Left       face      0.0658529 0.1097015 36231.43 -0.2235837  0.3552895   0.600  1.0000
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests

summary(contra_SC_P1[1:6], delta = delta_null, level = .9)

##  contrast            Hemisphere Category   estimate        SE       df   lower.CL   upper.CL t.ratio p.value
##  17_guess - 17_low   Left       face     -0.2452686 0.1354348 37115.10 -0.5695114  0.0789742  -1.881  0.1800
##  17_guess - 17_high  Left       face     -0.4319234 0.1376417 20687.49 -0.7614616 -0.1023852  -0.495  1.0000
##  17_guess - 200_high Left       face     -0.3660705 0.1425063 36491.93 -0.7072434 -0.0248975  -0.940  1.0000
##  17_low - 17_high    Left       face     -0.1866548 0.1094711 17691.79 -0.4487514  0.0754418  -2.862  0.0126
##  17_low - 200_high   Left       face     -0.1208019 0.1171315 37690.70 -0.4012249  0.1596212  -3.237  0.0036
##  17_high - 200_high  Left       face      0.0658529 0.1097015 36231.43 -0.1967824  0.3284882  -3.958  0.0002
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

summary(contra_SC_P1[1:6], delta = delta_null, side = ">")

##  contrast            Hemisphere Category   estimate        SE       df   lower.CL upper.CL t.ratio p.value
##  17_guess - 17_low   Left       face     -0.2452686 0.1354348 37115.10 -0.5695114      Inf   1.881  0.1800
##  17_guess - 17_high  Left       face     -0.4319234 0.1376417 20687.49 -0.7614616      Inf   0.495  1.0000
##  17_guess - 200_high Left       face     -0.3660705 0.1425063 36491.93 -0.7072434      Inf   0.940  1.0000
##  17_low - 17_high    Left       face     -0.1866548 0.1094711 17691.79 -0.4487514      Inf   2.862  0.0126
##  17_low - 200_high   Left       face     -0.1208019 0.1171315 37690.70 -0.4012249      Inf   3.237  0.0036
##  17_high - 200_high  Left       face      0.0658529 0.1097015 36231.43 -0.1967824      Inf   5.158  <.0001
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests 
## Statistics are tests of noninferiority with a threshold of 0.5 
## P values are right-tailed

summary(contra_SC_P1[1:6], delta = delta_null, side = "<")

##  contrast            Hemisphere Category   estimate        SE       df lower.CL   upper.CL t.ratio p.value
##  17_guess - 17_low   Left       face     -0.2452686 0.1354348 37115.10     -Inf  0.0789742  -5.503  <.0001
##  17_guess - 17_high  Left       face     -0.4319234 0.1376417 20687.49     -Inf -0.1023852  -6.771  <.0001
##  17_guess - 200_high Left       face     -0.3660705 0.1425063 36491.93     -Inf -0.0248975  -6.077  <.0001
##  17_low - 17_high    Left       face     -0.1866548 0.1094711 17691.79     -Inf  0.0754418  -6.272  <.0001
##  17_low - 200_high   Left       face     -0.1208019 0.1171315 37690.70     -Inf  0.1596212  -5.300  <.0001
##  17_high - 200_high  Left       face      0.0658529 0.1097015 36231.43     -Inf  0.3284882  -3.958  0.0002
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests 
## Statistics are tests of nonsuperiority with a threshold of 0.5 
## P values are left-tailed

Comparisons of P1 amplitudes for (intact) faces with different durations and confidence (right hemisphere):

contra_SC_P1[7:12]

##  contrast            Hemisphere Category   estimate        SE       df   lower.CL    upper.CL t.ratio p.value
##  17_guess - 17_low   Right      face     -0.2039626 0.1352749 35346.56 -0.5608726  0.15294740  -1.508  0.7897
##  17_guess - 17_high  Right      face     -0.5082257 0.1370905 15779.36 -0.8699512 -0.14650017  -3.707  0.0013
##  17_guess - 200_high Right      face     -0.5961750 0.1423218 34614.65 -0.9716781 -0.22067186  -4.189  0.0002
##  17_low - 17_high    Right      face     -0.3042631 0.1089672 13017.88 -0.5917906 -0.01673559  -2.792  0.0315
##  17_low - 200_high   Right      face     -0.3922124 0.1170154 36493.10 -0.7009459 -0.08347888  -3.352  0.0048
##  17_high - 200_high  Right      face     -0.0879493 0.1095568 34348.22 -0.3770051  0.20110645  -0.803  1.0000
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests

summary(contra_SC_P1[7:12], delta = delta_null, level = .9)

##  contrast            Hemisphere Category   estimate        SE       df   lower.CL    upper.CL t.ratio p.value
##  17_guess - 17_low   Right      face     -0.2039626 0.1352749 35346.56 -0.5278233  0.11989812  -2.188  0.0859
##  17_guess - 17_high  Right      face     -0.5082257 0.1370905 15779.36 -0.8364525 -0.17999890   0.060  1.0000
##  17_guess - 200_high Right      face     -0.5961750 0.1423218 34614.65 -0.9369071 -0.25544289   0.676  1.0000
##  17_low - 17_high    Right      face     -0.3042631 0.1089672 13017.88 -0.5651621 -0.04336406  -1.796  0.2174
##  17_low - 200_high   Right      face     -0.3922124 0.1170154 36493.10 -0.6723578 -0.11206702  -0.921  1.0000
##  17_high - 200_high  Right      face     -0.0879493 0.1095568 34348.22 -0.3502389  0.17434029  -3.761  0.0005
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

summary(contra_SC_P1[7:12], delta = delta_null, side = ">")

##  contrast            Hemisphere Category   estimate        SE       df   lower.CL upper.CL t.ratio p.value
##  17_guess - 17_low   Right      face     -0.2039626 0.1352749 35346.56 -0.5278233      Inf   2.188  0.0859
##  17_guess - 17_high  Right      face     -0.5082257 0.1370905 15779.36 -0.8364525      Inf  -0.060  1.0000
##  17_guess - 200_high Right      face     -0.5961750 0.1423218 34614.65 -0.9369071      Inf  -0.676  1.0000
##  17_low - 17_high    Right      face     -0.3042631 0.1089672 13017.88 -0.5651621      Inf   1.796  0.2174
##  17_low - 200_high   Right      face     -0.3922124 0.1170154 36493.10 -0.6723578      Inf   0.921  1.0000
##  17_high - 200_high  Right      face     -0.0879493 0.1095568 34348.22 -0.3502389      Inf   3.761  0.0005
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests 
## Statistics are tests of noninferiority with a threshold of 0.5 
## P values are right-tailed

summary(contra_SC_P1[7:12], delta = delta_null, side = "<")

##  contrast            Hemisphere Category   estimate        SE       df lower.CL    upper.CL t.ratio p.value
##  17_guess - 17_low   Right      face     -0.2039626 0.1352749 35346.56     -Inf  0.11989812  -5.204  <.0001
##  17_guess - 17_high  Right      face     -0.5082257 0.1370905 15779.36     -Inf -0.17999890  -7.354  <.0001
##  17_guess - 200_high Right      face     -0.5961750 0.1423218 34614.65     -Inf -0.25544289  -7.702  <.0001
##  17_low - 17_high    Right      face     -0.3042631 0.1089672 13017.88     -Inf -0.04336406  -7.381  <.0001
##  17_low - 200_high   Right      face     -0.3922124 0.1170154 36493.10     -Inf -0.11206702  -7.625  <.0001
##  17_high - 200_high  Right      face     -0.0879493 0.1095568 34348.22     -Inf  0.17434029  -5.367  <.0001
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests 
## Statistics are tests of nonsuperiority with a threshold of 0.5 
## P values are left-tailed

Comparisons of face-specific P1 amplitude components for different durations and confidence (left hemisphere):

contra_SC_P1_spec <- contrast(emmeans(lmm.opt.P1.SC.E205, ~ Category + DuraConf | Hemisphere), # 
                              interaction = "pairwise", adjust = "Bonferroni", infer = TRUE)

contra_SC_P1_spec[1:6]

##  Category_pairwise DuraConf_pairwise   Hemisphere    estimate        SE       df   lower.CL  upper.CL t.ratio p.value
##  face - house      17_guess - 17_low   Left       -0.29982473 0.1679815 24749.16 -0.7430388 0.1433893  -1.785  0.4458
##  face - house      17_guess - 17_high  Left       -0.21734758 0.1920036  5076.76 -0.7241011 0.2894060  -1.132  1.0000
##  face - house      17_guess - 200_high Left       -0.06479179 0.1800928 31977.11 -0.5399525 0.4103690  -0.360  1.0000
##  face - house      17_low - 17_high    Left        0.08247715 0.1716759  7492.37 -0.3705685 0.5355228   0.480  1.0000
##  face - house      17_low - 200_high   Left        0.23503294 0.1617098 38100.49 -0.1916213 0.6616872   1.453  0.8767
##  face - house      17_high - 200_high  Left        0.15255579 0.1814463 15301.12 -0.3262084 0.6313200   0.841  1.0000
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests

summary(contra_SC_P1_spec[1:6], delta = delta_null, level = .9)

##  Category_pairwise DuraConf_pairwise   Hemisphere    estimate        SE       df   lower.CL  upper.CL t.ratio p.value
##  face - house      17_guess - 17_low   Left       -0.29982473 0.1679815 24749.16 -0.7019964 0.1023469  -1.192  0.7002
##  face - house      17_guess - 17_high  Left       -0.21734758 0.1920036  5076.76 -0.6771527 0.2424576  -1.472  0.4232
##  face - house      17_guess - 200_high Left       -0.06479179 0.1800928 31977.11 -0.4959530 0.3663695  -2.417  0.0470
##  face - house      17_low - 17_high    Left        0.08247715 0.1716759  7492.37 -0.3286039 0.4935582  -2.432  0.0451
##  face - house      17_low - 200_high   Left        0.23503294 0.1617098 38100.49 -0.1521140 0.6221799  -1.639  0.3040
##  face - house      17_high - 200_high  Left        0.15255579 0.1814463 15301.12 -0.2818707 0.5869822  -1.915  0.1666
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

summary(contra_SC_P1_spec[1:6], delta = delta_null, side = ">")

##  Category_pairwise DuraConf_pairwise   Hemisphere    estimate        SE       df   lower.CL upper.CL t.ratio p.value
##  face - house      17_guess - 17_low   Left       -0.29982473 0.1679815 24749.16 -0.7019964      Inf   1.192  0.7002
##  face - house      17_guess - 17_high  Left       -0.21734758 0.1920036  5076.76 -0.6771527      Inf   1.472  0.4232
##  face - house      17_guess - 200_high Left       -0.06479179 0.1800928 31977.11 -0.4959530      Inf   2.417  0.0470
##  face - house      17_low - 17_high    Left        0.08247715 0.1716759  7492.37 -0.3286039      Inf   3.393  0.0021
##  face - house      17_low - 200_high   Left        0.23503294 0.1617098 38100.49 -0.1521140      Inf   4.545  <.0001
##  face - house      17_high - 200_high  Left        0.15255579 0.1814463 15301.12 -0.2818707      Inf   3.596  0.0010
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests 
## Statistics are tests of noninferiority with a threshold of 0.5 
## P values are right-tailed

summary(contra_SC_P1_spec[1:6], delta = delta_null, side = "<")

##  Category_pairwise DuraConf_pairwise   Hemisphere    estimate        SE       df lower.CL  upper.CL t.ratio p.value
##  face - house      17_guess - 17_low   Left       -0.29982473 0.1679815 24749.16     -Inf 0.1023469  -4.761  <.0001
##  face - house      17_guess - 17_high  Left       -0.21734758 0.1920036  5076.76     -Inf 0.2424576  -3.736  0.0006
##  face - house      17_guess - 200_high Left       -0.06479179 0.1800928 31977.11     -Inf 0.3663695  -3.136  0.0051
##  face - house      17_low - 17_high    Left        0.08247715 0.1716759  7492.37     -Inf 0.4935582  -2.432  0.0451
##  face - house      17_low - 200_high   Left        0.23503294 0.1617098 38100.49     -Inf 0.6221799  -1.639  0.3040
##  face - house      17_high - 200_high  Left        0.15255579 0.1814463 15301.12     -Inf 0.5869822  -1.915  0.1666
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests 
## Statistics are tests of nonsuperiority with a threshold of 0.5 
## P values are left-tailed

Comparisons of face-specific P1 amplitude components for different durations and confidence (right hemisphere):

contra_SC_P1_spec[7:12]

##  Category_pairwise DuraConf_pairwise   Hemisphere    estimate        SE       df   lower.CL  upper.CL t.ratio p.value
##  face - house      17_guess - 17_low   Right      -0.17513724 0.1680863 25502.49 -0.6186267 0.2683523  -1.042  1.0000
##  face - house      17_guess - 17_high  Right      -0.19073573 0.1923894  5541.46 -0.6984908 0.3170193  -0.991  1.0000
##  face - house      17_guess - 200_high Right      -0.22056491 0.1800024 31001.27 -0.6954880 0.2543582  -1.225  1.0000
##  face - house      17_low - 17_high    Right      -0.01559849 0.1719311  8082.09 -0.4693087 0.4381117  -0.091  1.0000
##  face - house      17_low - 200_high   Right      -0.04542767 0.1616989 37871.29 -0.4720533 0.3811979  -0.281  1.0000
##  face - house      17_high - 200_high  Right      -0.02982918 0.1818778 17940.88 -0.5097230 0.4500646  -0.164  1.0000
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests

summary(contra_SC_P1_spec[7:12], delta = delta_null, level = .9)

##  Category_pairwise DuraConf_pairwise   Hemisphere    estimate        SE       df   lower.CL  upper.CL t.ratio p.value
##  face - house      17_guess - 17_low   Right      -0.17513724 0.1680863 25502.49 -0.5775590 0.2272845  -1.933  0.1598
##  face - house      17_guess - 17_high  Right      -0.19073573 0.1923894  5541.46 -0.6514519 0.2699805  -1.607  0.3240
##  face - house      17_guess - 200_high Right      -0.22056491 0.1800024 31001.27 -0.6515104 0.2103806  -1.552  0.3617
##  face - house      17_low - 17_high    Right      -0.01559849 0.1719311  8082.09 -0.4272838 0.3960868  -2.817  0.0146
##  face - house      17_low - 200_high   Right      -0.04542767 0.1616989 37871.29 -0.4325487 0.3416933  -2.811  0.0148
##  face - house      17_high - 200_high  Right      -0.02982918 0.1818778 17940.88 -0.4652819 0.4056235  -2.585  0.0292
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

summary(contra_SC_P1_spec[7:12], delta = delta_null, side = ">")

##  Category_pairwise DuraConf_pairwise   Hemisphere    estimate        SE       df   lower.CL upper.CL t.ratio p.value
##  face - house      17_guess - 17_low   Right      -0.17513724 0.1680863 25502.49 -0.5775590      Inf   1.933  0.1598
##  face - house      17_guess - 17_high  Right      -0.19073573 0.1923894  5541.46 -0.6514519      Inf   1.607  0.3240
##  face - house      17_guess - 200_high Right      -0.22056491 0.1800024 31001.27 -0.6515104      Inf   1.552  0.3617
##  face - house      17_low - 17_high    Right      -0.01559849 0.1719311  8082.09 -0.4272838      Inf   2.817  0.0146
##  face - house      17_low - 200_high   Right      -0.04542767 0.1616989 37871.29 -0.4325487      Inf   2.811  0.0148
##  face - house      17_high - 200_high  Right      -0.02982918 0.1818778 17940.88 -0.4652819      Inf   2.585  0.0292
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests 
## Statistics are tests of noninferiority with a threshold of 0.5 
## P values are right-tailed

summary(contra_SC_P1_spec[7:12], delta = delta_null, side = "<")

##  Category_pairwise DuraConf_pairwise   Hemisphere    estimate        SE       df lower.CL  upper.CL t.ratio p.value
##  face - house      17_guess - 17_low   Right      -0.17513724 0.1680863 25502.49     -Inf 0.2272845  -4.017  0.0002
##  face - house      17_guess - 17_high  Right      -0.19073573 0.1923894  5541.46     -Inf 0.2699805  -3.590  0.0010
##  face - house      17_guess - 200_high Right      -0.22056491 0.1800024 31001.27     -Inf 0.2103806  -4.003  0.0002
##  face - house      17_low - 17_high    Right      -0.01559849 0.1719311  8082.09     -Inf 0.3960868  -2.999  0.0082
##  face - house      17_low - 200_high   Right      -0.04542767 0.1616989 37871.29     -Inf 0.3416933  -3.373  0.0022
##  face - house      17_high - 200_high  Right      -0.02982918 0.1818778 17940.88     -Inf 0.4056235  -2.913  0.0107
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests 
## Statistics are tests of nonsuperiority with a threshold of 0.5 
## P values are left-tailed

5.3.3.9.2 Other results

Comparisons of P1 amplitudes for (intact) faces with different durations and confidence (without multiple comparison corrections):

summary(contra_SC_P1[1:24], adjust = "none")

##  contrast            Hemisphere Category   estimate        SE       df   lower.CL   upper.CL t.ratio p.value
##  17_guess - 17_low   Left       face     -0.2452686 0.1354348 37115.10 -0.5107245  0.0201873  -1.811  0.0702
##  17_guess - 17_high  Left       face     -0.4319234 0.1376417 20687.49 -0.7017119 -0.1621349  -3.138  0.0017
##  17_guess - 200_high Left       face     -0.3660705 0.1425063 36491.93 -0.6453870 -0.0867540  -2.569  0.0102
##  17_low - 17_high    Left       face     -0.1866548 0.1094711 17691.79 -0.4012289  0.0279193  -1.705  0.0882
##  17_low - 200_high   Left       face     -0.1208019 0.1171315 37690.70 -0.3503828  0.1087791  -1.031  0.3024
##  17_high - 200_high  Left       face      0.0658529 0.1097015 36231.43 -0.1491652  0.2808710   0.600  0.5483
##  17_guess - 17_low   Right      face     -0.2039626 0.1352749 35346.56 -0.4691055  0.0611804  -1.508  0.1316
##  17_guess - 17_high  Right      face     -0.5082257 0.1370905 15779.36 -0.7769386 -0.2395127  -3.707  0.0002
##  17_guess - 200_high Right      face     -0.5961750 0.1423218 34614.65 -0.8751304 -0.3172196  -4.189  <.0001
##  17_low - 17_high    Right      face     -0.3042631 0.1089672 13017.88 -0.5178548 -0.0906714  -2.792  0.0052
##  17_low - 200_high   Right      face     -0.3922124 0.1170154 36493.10 -0.6215659 -0.1628589  -3.352  0.0008
##  17_high - 200_high  Right      face     -0.0879493 0.1095568 34348.22 -0.3026842  0.1267856  -0.803  0.4221
##  17_guess - 17_low   Left       house     0.0545561 0.1025349 14968.26 -0.1464249  0.2555372   0.532  0.5947
##  17_guess - 17_high  Left       house    -0.2145758 0.1458080  5305.47 -0.5004194  0.0712678  -1.472  0.1412
##  17_guess - 200_high Left       house    -0.3012787 0.1131173 34240.35 -0.5229923 -0.0795651  -2.663  0.0077
##  17_low - 17_high    Left       house    -0.2691319 0.1393197  9365.76 -0.5422287  0.0039649  -1.932  0.0534
##  17_low - 200_high   Left       house    -0.3558348 0.1115408 37181.39 -0.5744579 -0.1372118  -3.190  0.0014
##  17_high - 200_high  Left       house    -0.0867029 0.1482656 13912.45 -0.3773233  0.2039176  -0.585  0.5587
##  17_guess - 17_low   Right      house    -0.0288253 0.1030149 20852.20 -0.2307425  0.1730918  -0.280  0.7796
##  17_guess - 17_high  Right      house    -0.3174899 0.1471762  8696.05 -0.6059901 -0.0289898  -2.157  0.0310
##  17_guess - 200_high Right      house    -0.3756101 0.1132929 36512.03 -0.5976675 -0.1535527  -3.315  0.0009
##  17_low - 17_high    Right      house    -0.2886646 0.1402300 14313.22 -0.5635337 -0.0137955  -2.059  0.0396
##  17_low - 200_high   Right      house    -0.3467847 0.1116474 38261.87 -0.5656166 -0.1279528  -3.106  0.0019
##  17_high - 200_high  Right      house    -0.0581201 0.1490108 19565.78 -0.3501939  0.2339537  -0.390  0.6965
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95

summary(contra_SC_P1[1:24], adjust = "none", delta = delta_null, level = .9)

##  contrast            Hemisphere Category   estimate        SE       df   lower.CL   upper.CL t.ratio p.value
##  17_guess - 17_low   Left       face     -0.2452686 0.1354348 37115.10 -0.4680445 -0.0224927  -1.881  0.0300
##  17_guess - 17_high  Left       face     -0.4319234 0.1376417 20687.49 -0.6583339 -0.2055128  -0.495  0.3104
##  17_guess - 200_high Left       face     -0.3660705 0.1425063 36491.93 -0.6004784 -0.1316625  -0.940  0.1737
##  17_low - 17_high    Left       face     -0.1866548 0.1094711 17691.79 -0.3667282 -0.0065814  -2.862  0.0021
##  17_low - 200_high   Left       face     -0.1208019 0.1171315 37690.70 -0.3134708  0.0718671  -3.237  0.0006
##  17_high - 200_high  Left       face      0.0658529 0.1097015 36231.43 -0.1145946  0.2463004  -3.958  <.0001
##  17_guess - 17_low   Right      face     -0.2039626 0.1352749 35346.56 -0.4264758  0.0185506  -2.188  0.0143
##  17_guess - 17_high  Right      face     -0.5082257 0.1370905 15779.36 -0.7337326 -0.2827187   0.060  0.5239
##  17_guess - 200_high Right      face     -0.5961750 0.1423218 34614.65 -0.8302798 -0.3620702   0.676  0.7504
##  17_low - 17_high    Right      face     -0.3042631 0.1089672 13017.88 -0.4835110 -0.1250152  -1.796  0.0362
##  17_low - 200_high   Right      face     -0.3922124 0.1170154 36493.10 -0.5846905 -0.1997343  -0.921  0.1785
##  17_high - 200_high  Right      face     -0.0879493 0.1095568 34348.22 -0.2681591  0.0922604  -3.761  0.0001
##  17_guess - 17_low   Left       house     0.0545561 0.1025349 14968.26 -0.1141093  0.2232215  -4.344  <.0001
##  17_guess - 17_high  Left       house    -0.2145758 0.1458080  5305.47 -0.4544505  0.0252989  -1.958  0.0252
##  17_guess - 200_high Left       house    -0.3012787 0.1131173 34240.35 -0.4873450 -0.1152123  -1.757  0.0395
##  17_low - 17_high    Left       house    -0.2691319 0.1393197  9365.76 -0.4983151 -0.0399488  -1.657  0.0488
##  17_low - 200_high   Left       house    -0.3558348 0.1115408 37181.39 -0.5393077 -0.1723620  -1.292  0.0981
##  17_high - 200_high  Left       house    -0.0867029 0.1482656 13912.45 -0.3305943  0.1571885  -2.788  0.0027
##  17_guess - 17_low   Right      house    -0.0288253 0.1030149 20852.20 -0.1982772  0.1406266  -4.574  <.0001
##  17_guess - 17_high  Right      house    -0.3174899 0.1471762  8696.05 -0.5595990 -0.0753809  -1.240  0.1075
##  17_guess - 200_high Right      house    -0.3756101 0.1132929 36512.03 -0.5619651 -0.1892551  -1.098  0.1361
##  17_low - 17_high    Right      house    -0.2886646 0.1402300 14313.22 -0.5193374 -0.0579918  -1.507  0.0659
##  17_low - 200_high   Right      house    -0.3467847 0.1116474 38261.87 -0.5304329 -0.1631366  -1.372  0.0850
##  17_high - 200_high  Right      house    -0.0581201 0.1490108 19565.78 -0.3032326  0.1869924  -2.965  0.0015
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

5.3.4 Ampitudes of the N170

# only keep the data for amplitudes of the N170 (all erps)
df.erp.N170.SC.E2 <- {
  df.erp.SC.E2 %>% 
    filter(Component == "N170") 
}

if (saveCSV) {
  output.erp.N170.SC.E2 <- file.path(folder_nesi, "E205_erp_N170_SC.RData")
  save(df.erp.N170.SC.E2, file = output.erp.N170.SC.E2)
}

# the structure of this dataset
head(df.erp.N170.SC.E2, 10)

## # A tibble: 10 x 78
##    Channel Event Urevent TrialNumber Block_ST_table Response urResponse    RT  urRT Reject Rejthresh Rejjp SubjCode ChanCent ChanCluster_1 ChanCluster_2 ChanCluster_3 ChanCluster_4 ChanCluster_5 ChanCluster_6 ChanCluster_7 Hemisphere Component MeanAmp MethodCode Method        IsPositive StartFrame PeakFrame EndFrame StartTime PeakTime EndTime FrameSize WindowSize StartAmplitude PeakAmplitude EndAmplitude Session Block_beha_table Trial Resp_ACC Resp_OnsetDelay Resp_RT stimName   Type   Category Duration Stimuli Confidence DuraConf Type_C Cate_C Dura_C Type_Cate Type_Dura Cate_Dura Type_Cate_Dura Hemi_C Type_Hemi Cate_Hemi Dura_Hemi Type_Cate_Hemi Type_Dura_Hemi Cate_Dura_Hemi Type_Cate_Dura_Hemi ConLG_C ConHL_C Con2H_C Cate_ConLH Hemi_ConLH Cate_Hemi_ConLH Cate_ConGL Hemi_ConGL Cate_Hemi_ConGL Cate_Con2G Hemi_Con2G Cate_Hemi_Con2G
##    <chr>   <chr>   <dbl>       <dbl>          <dbl>    <dbl> <fct>      <dbl> <dbl>  <dbl>     <dbl> <dbl> <fct>    <chr>    <chr>         <chr>         <chr>         <chr>         <chr>         <chr>         <chr>         <fct>      <chr>       <dbl>      <dbl> <chr>              <dbl>      <dbl>     <dbl>    <dbl>     <dbl>    <dbl>   <dbl>     <dbl>      <dbl>          <dbl>         <dbl>        <dbl>   <dbl>            <dbl> <dbl>    <dbl>           <dbl>   <dbl> <chr>      <fct>  <fct>    <fct>    <chr>   <fct>      <fct>     <dbl>  <dbl>  <dbl>     <dbl>     <dbl>     <dbl>          <dbl>  <dbl>     <dbl>     <dbl>     <dbl>          <dbl>          <dbl>          <dbl>               <dbl>   <dbl>   <dbl>   <dbl>      <dbl>      <dbl>           <dbl>      <dbl>      <dbl>           <dbl>      <dbl>      <dbl>           <dbl>
##  1 E65     NF2+     2590         646              2       11 RE11         706   756      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       N170        -5.69          2 MeanAmplitude          0         38        43       48       152      172     192        10         40         -3.43         -10.2       -7.23         1               35    11        1              16     540 NfF040.bmp intact face     200      fF040   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.125     -0.125          0.0625      -0.25      -0.25           0.125      0.125      0.125         -0.0625
##  2 E65     NF2+     2610         651              2       11 RE11         564   614      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       N170        -2.28          2 MeanAmplitude          0         38        44       48       152      176     192        10         40         -0.667         -6.59       0.0234       1               35    16        1              17     397 NfF078.bmp intact face     200      fF078   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.125     -0.125          0.0625      -0.25      -0.25           0.125      0.125      0.125         -0.0625
##  3 E65     NF2+     2622         654              2       11 RE11         574   624      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       N170         3.67          2 MeanAmplitude          0         38        40       48       152      160     192        10         40          8.34           2.09       3.39         1               35    19        1              16     408 NfM122.bmp intact face     200      fM122   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.125     -0.125          0.0625      -0.25      -0.25           0.125      0.125      0.125         -0.0625
##  4 E65     NF2+     2626         655              2       11 RE11         387   437      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       N170         4.51          2 MeanAmplitude          0         38        41       48       152      164     192        10         40          7.44           3.17       8.05         1               35    20        1              17     220 NfM013.bmp intact face     200      fM013   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.125     -0.125          0.0625      -0.25      -0.25           0.125      0.125      0.125         -0.0625
##  5 E65     NF2+     2666         665              2       11 RE11         361   411      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       N170        -4.07          2 MeanAmplitude          0         38        43       48       152      172     192        10         40          2.85         -10.2       -4.62         1               35    30        1              16     195 NfM142.bmp intact face     200      fM142   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.125     -0.125          0.0625      -0.25      -0.25           0.125      0.125      0.125         -0.0625
##  6 E65     NF2+     2686         670              2       11 RE11         366   416      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       N170       -10.6           2 MeanAmplitude          0         38        40       48       152      160     192        10         40         -9.64         -11.6       -9.44         1               35    35        1              16     200 NfF112.bmp intact face     200      fF112   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.125     -0.125          0.0625      -0.25      -0.25           0.125      0.125      0.125         -0.0625
##  7 E65     NF2+     2690         671              2       11 RE11         423   473      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       N170         1.77          2 MeanAmplitude          0         38        43       48       152      172     192        10         40          0.612         -2.34       2.23         1               35    36        1              17     256 NfF017.bmp intact face     200      fF017   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.125     -0.125          0.0625      -0.25      -0.25           0.125      0.125      0.125         -0.0625
##  8 E65     NF2+     2722         679              2       11 RE11         446   496      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       N170         3.95          2 MeanAmplitude          0         38        41       48       152      164     192        10         40          5.19           1.21       7.96         1               35    44        1              17     279 NfF068.bmp intact face     200      fF068   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.125     -0.125          0.0625      -0.25      -0.25           0.125      0.125      0.125         -0.0625
##  9 E65     NF2+     2778         693              2       11 RE11         454   504      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       N170        -5.30          2 MeanAmplitude          0         38        48       48       152      192     192        10         40         -1.62          -7.56      -7.56         1               35    58        1              16     288 NfF076.bmp intact face     200      fF076   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.125     -0.125          0.0625      -0.25      -0.25           0.125      0.125      0.125         -0.0625
## 10 E65     NF2+     2790         696              2       11 RE11         441   491      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       N170        -1.28          2 MeanAmplitude          0         38        46       48       152      184     192        10         40         -1.24          -2.75      -1.00         1               35    61        1              16     275 NfF079.bmp intact face     200      fF079   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.125     -0.125          0.0625      -0.25      -0.25           0.125      0.125      0.125         -0.0625

5.3.4.1 The maximal model

lmm.max.N170.SC.E2 <- lmer(MeanAmp ~ Category * Hemisphere * DuraConf +
                             (1 + Cate_C + Hemi_C + Cate_Hemi | SubjCode) +
                             (1 + Hemi_C | Stimuli),
                           data = df.erp.N170.SC.E2,
                           verbose = TRUE,
                           control = lmerControl(optimizer = "bobyqa"))  # nloptwrap Nelder_Mead

## start par. =  1 0 1 1 0 0 0 1 0 0 1 0 1 fn =  222383.5 
## At return
## eval: 1455 fn:      221500.89 par: 0.0377452 0.00592604 1.27875e-07 0.468916 -0.124988 -0.00676600 -0.0181415 0.172582 -0.0131822 -0.0558530 0.351847 -0.213334 0.184849

print(summary(lmm.max.N170.SC.E2), corr = FALSE)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category * Hemisphere * DuraConf + (1 + Cate_C + Hemi_C +      Cate_Hemi | SubjCode) + (1 + Hemi_C | Stimuli)
##    Data: df.erp.N170.SC.E2
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 221500.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -7.3165 -0.5946  0.0000  0.5895  6.7424 
## 
## Random effects:
##  Groups   Name        Variance  Std.Dev. Corr             
##  Stimuli  (Intercept) 2.217e-02 0.14888                   
##           Hemi_C      5.464e-04 0.02337  1.00             
##  SubjCode (Intercept) 3.421e+00 1.84956                   
##           Cate_C      7.064e-01 0.84049  -0.59            
##           Hemi_C      1.929e+00 1.38903  -0.02 -0.02      
##           Cate_Hemi   1.293e+00 1.13724  -0.06 -0.12 -0.73
##  Residual             1.556e+01 3.94434                   
## Number of obs: 39600, groups:  Stimuli, 80; SubjCode, 28
## 
## Fixed effects:
##                                         Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)                           -1.996e+00  3.506e-01  2.714e+01  -5.693 4.68e-06 ***
## Category2-1                            1.217e+00  1.682e-01  2.946e+01   7.236 5.24e-08 ***
## Hemisphere2-1                         -1.775e-01  2.661e-01  2.726e+01  -0.667  0.51039    
## DuraConf2-1                           -4.935e-01  6.263e-02  3.817e+04  -7.880 3.35e-15 ***
## DuraConf3-2                           -1.805e-01  6.747e-02  3.568e+04  -2.675  0.00747 ** 
## DuraConf4-3                            3.715e-01  6.865e-02  3.638e+04   5.412 6.29e-08 ***
## Category2-1:Hemisphere2-1              3.189e-01  2.322e-01  2.856e+01   1.373  0.18036    
## Category2-1:DuraConf2-1                5.042e-01  1.248e-01  3.307e+04   4.039 5.37e-05 ***
## Category2-1:DuraConf3-2                3.517e-01  1.335e-01  1.716e+04   2.635  0.00843 ** 
## Category2-1:DuraConf4-3                1.770e+00  1.365e-01  2.594e+04  12.971  < 2e-16 ***
## Hemisphere2-1:DuraConf2-1             -1.560e-03  1.242e-01  3.044e+04  -0.013  0.98998    
## Hemisphere2-1:DuraConf3-2             -2.377e-01  1.329e-01  1.699e+04  -1.789  0.07358 .  
## Hemisphere2-1:DuraConf4-3             -1.685e-01  1.354e-01  1.950e+04  -1.244  0.21353    
## Category2-1:Hemisphere2-1:DuraConf2-1  2.736e-01  2.465e-01  1.737e+04   1.110  0.26707    
## Category2-1:Hemisphere2-1:DuraConf3-2 -5.786e-02  2.597e-01  4.298e+03  -0.223  0.82372    
## Category2-1:Hemisphere2-1:DuraConf4-3  2.674e-01  2.675e-01  8.591e+03   1.000  0.31754    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular

5.3.4.2 The zero-correlation-parameter model

lmm.zcp.N170.SC.E2 <- update(lmm.max.N170.SC.E2,
                             formula = MeanAmp ~ Category * Hemisphere * DuraConf +
                               (1 + Cate_C + Hemi_C + Cate_Hemi || SubjCode) +
                               (1 + Hemi_C || Stimuli))

## start par. =  1 1 1 1 1 1 fn =  222383.5 
## At return
## eval: 464 fn:      221530.50 par:  0.00000 0.0376425 0.288392 0.352220 0.212488 0.469156

print(summary(lmm.zcp.N170.SC.E2), corr = FALSE)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category + Hemisphere + DuraConf + (1 + Cate_C + Hemi_C +      Cate_Hemi || SubjCode) + (1 + Hemi_C || Stimuli) + Category:Hemisphere +      Category:DuraConf + Hemisphere:DuraConf + Category:Hemisphere:DuraConf
##    Data: df.erp.N170.SC.E2
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 221530.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -7.3076 -0.5935 -0.0001  0.5902  6.7081 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev.
##  Stimuli    Hemi_C       0.00000 0.0000  
##  Stimuli.1  (Intercept)  0.02205 0.1485  
##  SubjCode   Cate_Hemi    1.29397 1.1375  
##  SubjCode.1 Hemi_C       1.93012 1.3893  
##  SubjCode.2 Cate_C       0.70247 0.8381  
##  SubjCode.3 (Intercept)  3.42446 1.8505  
##  Residual               15.55810 3.9444  
## Number of obs: 39600, groups:  Stimuli, 80; SubjCode, 28
## 
## Fixed effects:
##                                         Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)                           -1.995e+00  3.508e-01  2.714e+01  -5.687 4.76e-06 ***
## Category2-1                            1.217e+00  1.677e-01  2.946e+01   7.253 5.00e-08 ***
## Hemisphere2-1                         -1.757e-01  2.662e-01  2.726e+01  -0.660  0.51480    
## DuraConf2-1                           -4.958e-01  6.270e-02  3.886e+04  -7.909 2.67e-15 ***
## DuraConf3-2                           -1.785e-01  6.761e-02  3.821e+04  -2.640  0.00829 ** 
## DuraConf4-3                            3.698e-01  6.879e-02  3.855e+04   5.376 7.66e-08 ***
## Category2-1:Hemisphere2-1              3.189e-01  2.323e-01  2.856e+01   1.373  0.18044    
## Category2-1:DuraConf2-1                5.018e-01  1.251e-01  3.672e+04   4.011 6.05e-05 ***
## Category2-1:DuraConf3-2                3.702e-01  1.343e-01  2.615e+04   2.757  0.00584 ** 
## Category2-1:DuraConf4-3                1.759e+00  1.371e-01  3.347e+04  12.835  < 2e-16 ***
## Hemisphere2-1:DuraConf2-1              3.713e-03  1.248e-01  3.817e+04   0.030  0.97626    
## Hemisphere2-1:DuraConf3-2             -2.309e-01  1.342e-01  3.377e+04  -1.721  0.08521 .  
## Hemisphere2-1:DuraConf4-3             -1.775e-01  1.368e-01  3.704e+04  -1.298  0.19430    
## Category2-1:Hemisphere2-1:DuraConf2-1  2.960e-01  2.486e-01  3.101e+04   1.191  0.23377    
## Category2-1:Hemisphere2-1:DuraConf3-2 -3.908e-02  2.652e-01  1.341e+04  -0.147  0.88287    
## Category2-1:Hemisphere2-1:DuraConf4-3  2.456e-01  2.718e-01  2.410e+04   0.904  0.36614    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular

anova(lmm.max.N170.SC.E2, lmm.zcp.N170.SC.E2, refit = FALSE)

## Data: df.erp.N170.SC.E2
## Models:
## lmm.zcp.N170.SC.E2: MeanAmp ~ Category + Hemisphere + DuraConf + (1 + Cate_C + Hemi_C + 
## lmm.zcp.N170.SC.E2:     Cate_Hemi || SubjCode) + (1 + Hemi_C || Stimuli) + Category:Hemisphere + 
## lmm.zcp.N170.SC.E2:     Category:DuraConf + Hemisphere:DuraConf + Category:Hemisphere:DuraConf
## lmm.max.N170.SC.E2: MeanAmp ~ Category * Hemisphere * DuraConf + (1 + Cate_C + Hemi_C + 
## lmm.max.N170.SC.E2:     Cate_Hemi | SubjCode) + (1 + Hemi_C | Stimuli)
##                    npar    AIC    BIC  logLik deviance  Chisq Df Pr(>Chisq)    
## lmm.zcp.N170.SC.E2   23 221576 221774 -110765   221530                         
## lmm.max.N170.SC.E2   30 221561 221818 -110750   221501 29.605  7  0.0001122 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.4.3 The reduced model

summary(rePCA(lmm.zcp.N170.SC.E2))

## $Stimuli
## Importance of components:
##                           [,1] [,2]
## Standard deviation     0.03764    0
## Proportion of Variance 1.00000    0
## Cumulative Proportion  1.00000    1
## 
## $SubjCode
## Importance of components:
##                          [,1]   [,2]   [,3]    [,4]
## Standard deviation     0.4692 0.3522 0.2884 0.21249
## Proportion of Variance 0.4658 0.2626 0.1760 0.09556
## Cumulative Proportion  0.4658 0.7284 0.9044 1.00000

lmm.rdc.N170.SC.E2 <- update(lmm.zcp.N170.SC.E2,
                             formula = MeanAmp ~ Category * Hemisphere * DuraConf +
                               (1 + Cate_C + Hemi_C + Cate_Hemi || SubjCode) +
                               (1 | Stimuli))

## start par. =  1 1 1 1 1 fn =  222042.5 
## At return
## eval: 394 fn:      221530.50 par: 0.0376426 0.288391 0.352220 0.212489 0.469156

print(summary(lmm.rdc.N170.SC.E2), corr = FALSE)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category + Hemisphere + DuraConf + (1 + Cate_C + Hemi_C +      Cate_Hemi || SubjCode) + (1 | Stimuli) + Category:Hemisphere +      Category:DuraConf + Hemisphere:DuraConf + Category:Hemisphere:DuraConf
##    Data: df.erp.N170.SC.E2
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 221530.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -7.3076 -0.5935 -0.0001  0.5902  6.7081 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev.
##  Stimuli    (Intercept)  0.02205 0.1485  
##  SubjCode   Cate_Hemi    1.29396 1.1375  
##  SubjCode.1 Hemi_C       1.93012 1.3893  
##  SubjCode.2 Cate_C       0.70247 0.8381  
##  SubjCode.3 (Intercept)  3.42446 1.8505  
##  Residual               15.55810 3.9444  
## Number of obs: 39600, groups:  Stimuli, 80; SubjCode, 28
## 
## Fixed effects:
##                                         Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)                           -1.995e+00  3.508e-01  2.714e+01  -5.687 4.76e-06 ***
## Category2-1                            1.217e+00  1.677e-01  2.946e+01   7.253 5.00e-08 ***
## Hemisphere2-1                         -1.757e-01  2.662e-01  2.726e+01  -0.660  0.51480    
## DuraConf2-1                           -4.958e-01  6.270e-02  3.886e+04  -7.909 2.67e-15 ***
## DuraConf3-2                           -1.785e-01  6.761e-02  3.821e+04  -2.640  0.00829 ** 
## DuraConf4-3                            3.698e-01  6.879e-02  3.855e+04   5.376 7.66e-08 ***
## Category2-1:Hemisphere2-1              3.189e-01  2.323e-01  2.856e+01   1.373  0.18044    
## Category2-1:DuraConf2-1                5.018e-01  1.251e-01  3.672e+04   4.011 6.05e-05 ***
## Category2-1:DuraConf3-2                3.702e-01  1.343e-01  2.615e+04   2.757  0.00584 ** 
## Category2-1:DuraConf4-3                1.759e+00  1.371e-01  3.347e+04  12.835  < 2e-16 ***
## Hemisphere2-1:DuraConf2-1              3.713e-03  1.248e-01  3.817e+04   0.030  0.97626    
## Hemisphere2-1:DuraConf3-2             -2.309e-01  1.342e-01  3.377e+04  -1.721  0.08521 .  
## Hemisphere2-1:DuraConf4-3             -1.775e-01  1.368e-01  3.704e+04  -1.298  0.19430    
## Category2-1:Hemisphere2-1:DuraConf2-1  2.960e-01  2.486e-01  3.101e+04   1.191  0.23377    
## Category2-1:Hemisphere2-1:DuraConf3-2 -3.908e-02  2.652e-01  1.341e+04  -0.147  0.88287    
## Category2-1:Hemisphere2-1:DuraConf4-3  2.456e-01  2.718e-01  2.410e+04   0.904  0.36614    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

anova(lmm.rdc.N170.SC.E2, lmm.zcp.N170.SC.E2, refit = FALSE)

## Data: df.erp.N170.SC.E2
## Models:
## lmm.rdc.N170.SC.E2: MeanAmp ~ Category + Hemisphere + DuraConf + (1 + Cate_C + Hemi_C + 
## lmm.rdc.N170.SC.E2:     Cate_Hemi || SubjCode) + (1 | Stimuli) + Category:Hemisphere + 
## lmm.rdc.N170.SC.E2:     Category:DuraConf + Hemisphere:DuraConf + Category:Hemisphere:DuraConf
## lmm.zcp.N170.SC.E2: MeanAmp ~ Category + Hemisphere + DuraConf + (1 + Cate_C + Hemi_C + 
## lmm.zcp.N170.SC.E2:     Cate_Hemi || SubjCode) + (1 + Hemi_C || Stimuli) + Category:Hemisphere + 
## lmm.zcp.N170.SC.E2:     Category:DuraConf + Hemisphere:DuraConf + Category:Hemisphere:DuraConf
##                    npar    AIC    BIC  logLik deviance Chisq Df Pr(>Chisq)
## lmm.rdc.N170.SC.E2   22 221574 221763 -110765   221530                    
## lmm.zcp.N170.SC.E2   23 221576 221774 -110765   221530     0  1          1

5.3.4.4 The extended model

lmm.etd.N170.SC.E2 <- update(lmm.rdc.N170.SC.E2,
                             formula = MeanAmp ~ Category * Hemisphere * DuraConf +
                               (1 + Cate_C + Hemi_C + Cate_Hemi | SubjCode) +
                               (1 | Stimuli))

## start par. =  1 1 0 0 0 1 0 0 1 0 1 fn =  222042.5 
## At return
## eval: 1146 fn:      221501.14 par: 0.0376527 0.468917 -0.124977 -0.00678833 -0.0181633 0.172587 -0.0131156 -0.0558089 0.351828 -0.213357 0.184805

print(summary(lmm.rdc.N170.SC.E2), corr = FALSE)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category + Hemisphere + DuraConf + (1 + Cate_C + Hemi_C +      Cate_Hemi || SubjCode) + (1 | Stimuli) + Category:Hemisphere +      Category:DuraConf + Hemisphere:DuraConf + Category:Hemisphere:DuraConf
##    Data: df.erp.N170.SC.E2
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 221530.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -7.3076 -0.5935 -0.0001  0.5902  6.7081 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev.
##  Stimuli    (Intercept)  0.02205 0.1485  
##  SubjCode   Cate_Hemi    1.29396 1.1375  
##  SubjCode.1 Hemi_C       1.93012 1.3893  
##  SubjCode.2 Cate_C       0.70247 0.8381  
##  SubjCode.3 (Intercept)  3.42446 1.8505  
##  Residual               15.55810 3.9444  
## Number of obs: 39600, groups:  Stimuli, 80; SubjCode, 28
## 
## Fixed effects:
##                                         Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)                           -1.995e+00  3.508e-01  2.714e+01  -5.687 4.76e-06 ***
## Category2-1                            1.217e+00  1.677e-01  2.946e+01   7.253 5.00e-08 ***
## Hemisphere2-1                         -1.757e-01  2.662e-01  2.726e+01  -0.660  0.51480    
## DuraConf2-1                           -4.958e-01  6.270e-02  3.886e+04  -7.909 2.67e-15 ***
## DuraConf3-2                           -1.785e-01  6.761e-02  3.821e+04  -2.640  0.00829 ** 
## DuraConf4-3                            3.698e-01  6.879e-02  3.855e+04   5.376 7.66e-08 ***
## Category2-1:Hemisphere2-1              3.189e-01  2.323e-01  2.856e+01   1.373  0.18044    
## Category2-1:DuraConf2-1                5.018e-01  1.251e-01  3.672e+04   4.011 6.05e-05 ***
## Category2-1:DuraConf3-2                3.702e-01  1.343e-01  2.615e+04   2.757  0.00584 ** 
## Category2-1:DuraConf4-3                1.759e+00  1.371e-01  3.347e+04  12.835  < 2e-16 ***
## Hemisphere2-1:DuraConf2-1              3.713e-03  1.248e-01  3.817e+04   0.030  0.97626    
## Hemisphere2-1:DuraConf3-2             -2.309e-01  1.342e-01  3.377e+04  -1.721  0.08521 .  
## Hemisphere2-1:DuraConf4-3             -1.775e-01  1.368e-01  3.704e+04  -1.298  0.19430    
## Category2-1:Hemisphere2-1:DuraConf2-1  2.960e-01  2.486e-01  3.101e+04   1.191  0.23377    
## Category2-1:Hemisphere2-1:DuraConf3-2 -3.908e-02  2.652e-01  1.341e+04  -0.147  0.88287    
## Category2-1:Hemisphere2-1:DuraConf4-3  2.456e-01  2.718e-01  2.410e+04   0.904  0.36614    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

anova(lmm.rdc.N170.SC.E2, lmm.etd.N170.SC.E2, refit = FALSE)

## Data: df.erp.N170.SC.E2
## Models:
## lmm.rdc.N170.SC.E2: MeanAmp ~ Category + Hemisphere + DuraConf + (1 + Cate_C + Hemi_C + 
## lmm.rdc.N170.SC.E2:     Cate_Hemi || SubjCode) + (1 | Stimuli) + Category:Hemisphere + 
## lmm.rdc.N170.SC.E2:     Category:DuraConf + Hemisphere:DuraConf + Category:Hemisphere:DuraConf
## lmm.etd.N170.SC.E2: MeanAmp ~ Category + Hemisphere + DuraConf + (1 + Cate_C + Hemi_C + 
## lmm.etd.N170.SC.E2:     Cate_Hemi | SubjCode) + (1 | Stimuli) + Category:Hemisphere + 
## lmm.etd.N170.SC.E2:     Category:DuraConf + Hemisphere:DuraConf + Category:Hemisphere:DuraConf
##                    npar    AIC    BIC  logLik deviance  Chisq Df Pr(>Chisq)    
## lmm.rdc.N170.SC.E2   22 221574 221763 -110765   221530                         
## lmm.etd.N170.SC.E2   28 221557 221798 -110751   221501 29.357  6  5.207e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.4.5 The optimal model

lmm.opt.N170.SC.E205 <- lmm.etd.N170.SC.E2

print(summary(lmm.opt.N170.SC.E205), corr = FALSE)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category + Hemisphere + DuraConf + (1 + Cate_C + Hemi_C +      Cate_Hemi | SubjCode) + (1 | Stimuli) + Category:Hemisphere +      Category:DuraConf + Hemisphere:DuraConf + Category:Hemisphere:DuraConf
##    Data: df.erp.N170.SC.E2
## Control: lmerControl(optimizer = "bobyqa")
## 
## REML criterion at convergence: 221501.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -7.3165 -0.5946 -0.0004  0.5897  6.7418 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr             
##  Stimuli  (Intercept)  0.02206 0.1485                    
##  SubjCode (Intercept)  3.42096 1.8496                    
##           Cate_C       0.70642 0.8405   -0.59            
##           Hemi_C       1.92921 1.3890   -0.02 -0.02      
##           Cate_Hemi    1.29317 1.1372   -0.06 -0.12 -0.73
##  Residual             15.55805 3.9444                    
## Number of obs: 39600, groups:  Stimuli, 80; SubjCode, 28
## 
## Fixed effects:
##                                         Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)                           -1.996e+00  3.506e-01  2.714e+01  -5.693 4.68e-06 ***
## Category2-1                            1.217e+00  1.681e-01  2.945e+01   7.236 5.24e-08 ***
## Hemisphere2-1                         -1.775e-01  2.661e-01  2.725e+01  -0.667  0.51030    
## DuraConf2-1                           -4.936e-01  6.263e-02  3.817e+04  -7.882 3.31e-15 ***
## DuraConf3-2                           -1.805e-01  6.747e-02  3.568e+04  -2.674  0.00749 ** 
## DuraConf4-3                            3.715e-01  6.865e-02  3.638e+04   5.411 6.29e-08 ***
## Category2-1:Hemisphere2-1              3.184e-01  2.321e-01  2.853e+01   1.372  0.18085    
## Category2-1:DuraConf2-1                5.042e-01  1.248e-01  3.307e+04   4.040 5.36e-05 ***
## Category2-1:DuraConf3-2                3.517e-01  1.335e-01  1.717e+04   2.634  0.00844 ** 
## Category2-1:DuraConf4-3                1.770e+00  1.365e-01  2.596e+04  12.970  < 2e-16 ***
## Hemisphere2-1:DuraConf2-1             -3.097e-03  1.242e-01  3.044e+04  -0.025  0.98010    
## Hemisphere2-1:DuraConf3-2             -2.384e-01  1.329e-01  1.699e+04  -1.794  0.07281 .  
## Hemisphere2-1:DuraConf4-3             -1.673e-01  1.354e-01  1.950e+04  -1.235  0.21683    
## Category2-1:Hemisphere2-1:DuraConf2-1  2.733e-01  2.465e-01  1.737e+04   1.109  0.26762    
## Category2-1:Hemisphere2-1:DuraConf3-2 -5.872e-02  2.597e-01  4.297e+03  -0.226  0.82116    
## Category2-1:Hemisphere2-1:DuraConf4-3  2.686e-01  2.675e-01  8.591e+03   1.004  0.31538    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

anova(lmm.opt.N170.SC.E205)

## Type III Analysis of Variance Table with Satterthwaite's method
##                              Sum Sq Mean Sq NumDF DenDF  F value    Pr(>F)    
## Category                      814.7  814.68     1    29  52.3637  5.24e-08 ***
## Hemisphere                      6.9    6.92     1    27   0.4450  0.510305    
## DuraConf                     1461.3  487.09     3 37552  31.3076 < 2.2e-16 ***
## Category:Hemisphere            29.3   29.27     1    29   1.8815  0.180850    
## Category:DuraConf            7634.3 2544.78     3 26542 163.5668 < 2.2e-16 ***
## Hemisphere:DuraConf           229.9   76.63     3 25808   4.9255  0.002022 ** 
## Category:Hemisphere:DuraConf   54.5   18.17     3  9689   1.1678  0.320372    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.3.4.6 Diagnostic plots

qqplot_lmer(lmm.opt.N170.SC.E205)

5.3.4.7 Estimated marginal means

emm.N170.SC.E205 <- emmeans(lmm.opt.N170.SC.E205, ~ Category + DuraConf + Hemisphere)

emm.N170.SC.E205 %>% 
  as_tibble() %>% 
  arrange(Category, DuraConf)

## # A tibble: 16 x 8
##    Category DuraConf Hemisphere emmean    SE    df lower.CL upper.CL
##    <fct>    <fct>    <fct>       <dbl> <dbl> <dbl>    <dbl>    <dbl>
##  1 face     17_guess Left       -1.71  0.454  30.7    -2.64  -0.786 
##  2 face     17_guess Right      -1.76  0.454  30.7    -2.69  -0.839 
##  3 face     17_low   Left       -2.39  0.446  28.6    -3.30  -1.48  
##  4 face     17_low   Right      -2.58  0.446  28.6    -3.49  -1.67  
##  5 face     17_high  Left       -2.64  0.444  28.1    -3.55  -1.73  
##  6 face     17_high  Right      -3.04  0.444  28.1    -3.95  -2.13  
##  7 face     200_high Left       -3.00  0.447  28.9    -3.92  -2.09  
##  8 face     200_high Right      -3.71  0.447  28.9    -4.62  -2.79  
##  9 house    17_guess Left       -1.53  0.343  29.2    -2.23  -0.829 
## 10 house    17_guess Right      -1.51  0.326  29.3    -2.17  -0.840 
## 11 house    17_low   Left       -1.84  0.342  28.9    -2.54  -1.14  
## 12 house    17_low   Right      -1.68  0.325  29.0    -2.35  -1.02  
## 13 house    17_high  Left       -1.71  0.358  34.7    -2.44  -0.982 
## 14 house    17_high  Right      -1.82  0.342  35.2    -2.51  -1.13  
## 15 house    200_high Left       -0.436 0.346  30.3    -1.14   0.270 
## 16 house    200_high Right      -0.580 0.330  30.6    -1.25   0.0927

5.3.4.8 Plots

# emmip(emm.N170.SC.E205, Category ~ DuraConf | Hemisphere, CIs = TRUE) + 
#   scale_y_continuous(trans = "reverse")

duraconf.labels = c("17ms\nguess", "17ms\nlow", "17ms\nhigh", "200ms\nhigh")

n170.conf.LinePlot = {
  ggplot(data = data.frame(emm.N170.SC.E205), aes(y = emmean, x = DuraConf, color = Category, group = Category)) +  # set the data, varialbes for x and y axises, and the variable for dividing data
    geom_point(size = 2) +
    geom_line(aes(linetype = Category), size = .85) +  # position = "dodge", alpha = .7
    scale_linetype_manual(values=c("solid", "dashed")) +
    geom_errorbar(mapping = aes(ymin = lower.CL, ymax = upper.CL), linetype = 1,  # set the error bar
                  show.legend = FALSE, width = 0.25, alpha = .5) + # ,                   position = position_dodge(width=0.9)
    facet_grid(. ~ Hemisphere, # switch = "x",
               labeller = labeller(Hemisphere = hemiLabel)) +
    scale_y_reverse(limits = ylimit_n170, breaks = seq(1, -5, -1)) +  # set the limit for y axis
    scale_x_discrete(labels = duraconf.labels) +
    # scale_y_continuous(expand= c(0, 0)) +  # remove the space between columns and x axis
    labs(x = "Duration and Subjective Confidence", y = expression(paste("Amplitudes (", mu, "V)")), fill = "Category") +  # set the names for main, x and y axises title = "", 
    # scale_x_discrete(labels = c("left", "right")) +
    # geom_text(label = c("", "", "", "", "***", "", "", ""), color = "red", size = 6, nudge_y = 0.5, nudge_x = 0.5) + # add starts to the significant columns
    theme_bw() +
    erp_theme
}

# n170.conf.LinePlot.slide <- {
#   n170.conf.LinePlot + 
#     scale_y_reverse(limits = c(0.8, -4.8), breaks = seq(0, -4, -1)) +  # set the limit for y axis
#     theme(legend.position="right",
#           axis.line = element_line(color="black", size = 3))
# }
# ggsave('N170_confi.png', n170.conf.LinePlot.slide, width = 8.5, height = 4)

n170.conf.LinePlot

duration_color <- c(brewer.pal(9, 'Blues')[c(6, 7, 9)], brewer.pal(12, "Paired")[6]) 
category_color <- brewer.pal(8, 'Dark2')[c(6,1)]

n170_con_LinePlot = {
  ggplot(data = data.frame(emm.N170.SC.E205), aes(y = emmean, x = DuraConf, group = Category)) +  # set the data, varialbes for x and y axises, and the variable for dividing data
    geom_point(aes(fill = DuraConf), size=4, shape=21, stroke=0) +
    scale_fill_manual(values = duration_color, guide = FALSE) +
    geom_line(aes(linetype = Category, color = Category), size = .85) +  # position = "dodge", alpha = .7
    scale_color_manual(values=category_color) +
    scale_linetype_manual(values=c("solid", "dashed")) +
    geom_errorbar(mapping = aes(ymin = lower.CL, ymax = upper.CL), linetype = 1,  # set the error bar
                  show.legend = FALSE, width = 0.25, alpha = .5) + # ,                   position = position_dodge(width=0.9)
    facet_grid(. ~ Hemisphere, # switch = "x",
               labeller = labeller(Hemisphere = hemiLabel)) +
    scale_y_reverse(limits = ylimit_n170, breaks = seq(1, -5, -1)) +  # set the limit for y axis
    scale_x_discrete(labels = duraconf.labels) +
    # scale_y_continuous(expand= c(0, 0)) +  # remove the space between columns and x axis
    labs(x = "Duration and Subjective Confidence", y = expression(paste("N170 Amplitudes (", mu, "V)")), fill = "Category") +  # set the names for main, x and y axises title = "", 
    theme_bw() +
    erp_theme +
    theme(
      axis.title.x = element_text(margin = margin(t = 10, r = 0, b = 0, l = 0)),
      legend.margin = margin(t = -10),
      legend.title=element_text(size=12), 
      legend.text=element_text(size=12),
    )
}

ggsave('N170_con_pub.pdf', n170_con_LinePlot, width = 7, height = 5)

5.3.4.9 Contrast

5.3.4.9.1 Main results

Comparisons of N170 amplitudes for (intact) faces with different durations and confidence (left hemisphere):

contra_SC_N170 <- contrast(emmeans(lmm.opt.N170.SC.E205, ~ DuraConf | Hemisphere + Category), # 
                               "pairwise", adjust = "Bonferroni", infer = TRUE)

contra_SC_N170[1:6]

##  contrast            Hemisphere Category  estimate        SE       df   lower.CL  upper.CL t.ratio p.value
##  17_guess - 17_low   Left       face     0.6758619 0.1395425 38713.15  0.3076941 1.0440298   4.843  <.0001
##  17_guess - 17_high  Left       face     0.9276422 0.1460076 29514.59  0.5424106 1.3128739   6.353  <.0001
##  17_guess - 200_high Left       face     1.2904477 0.1469225 38302.54  0.9028082 1.6780872   8.783  <.0001
##  17_low - 17_high    Left       face     0.2517803 0.1169049 27206.64 -0.0566675 0.5602280   2.154  0.1876
##  17_low - 200_high   Left       face     0.6145858 0.1203932 38772.85  0.2969414 0.9322302   5.105  <.0001
##  17_high - 200_high  Left       face     0.3628055 0.1131308 38093.91  0.0643219 0.6612891   3.207  0.0081
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests

summary(contra_SC_N170[1:6], delta = delta_null, level = .9)

##  contrast            Hemisphere Category  estimate        SE       df   lower.CL  upper.CL t.ratio p.value
##  17_guess - 17_low   Left       face     0.6758619 0.1395425 38713.15  0.3417856 1.0099383   1.260  1.0000
##  17_guess - 17_high  Left       face     0.9276422 0.1460076 29514.59  0.5780830 1.2772015   2.929  1.0000
##  17_guess - 200_high Left       face     1.2904477 0.1469225 38302.54  0.9387028 1.6421927   5.380  1.0000
##  17_low - 17_high    Left       face     0.2517803 0.1169049 27206.64 -0.0281050 0.5316656  -2.123  0.1012
##  17_low - 200_high   Left       face     0.6145858 0.1203932 38772.85  0.3263545 0.9028171   0.952  1.0000
##  17_high - 200_high  Left       face     0.3628055 0.1131308 38093.91  0.0919608 0.6336502  -1.213  0.6757
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

summary(contra_SC_N170[1:6], delta = delta_null, side = ">")

##  contrast            Hemisphere Category  estimate        SE       df   lower.CL upper.CL t.ratio p.value
##  17_guess - 17_low   Left       face     0.6758619 0.1395425 38713.15  0.3417856      Inf   8.427  <.0001
##  17_guess - 17_high  Left       face     0.9276422 0.1460076 29514.59  0.5780830      Inf   9.778  <.0001
##  17_guess - 200_high Left       face     1.2904477 0.1469225 38302.54  0.9387028      Inf  12.186  <.0001
##  17_low - 17_high    Left       face     0.2517803 0.1169049 27206.64 -0.0281050      Inf   6.431  <.0001
##  17_low - 200_high   Left       face     0.6145858 0.1203932 38772.85  0.3263545      Inf   9.258  <.0001
##  17_high - 200_high  Left       face     0.3628055 0.1131308 38093.91  0.0919608      Inf   7.627  <.0001
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests 
## Statistics are tests of noninferiority with a threshold of 0.5 
## P values are right-tailed

summary(contra_SC_N170[1:6], delta = delta_null, side = "<")

##  contrast            Hemisphere Category  estimate        SE       df lower.CL  upper.CL t.ratio p.value
##  17_guess - 17_low   Left       face     0.6758619 0.1395425 38713.15     -Inf 1.0099383   1.260  1.0000
##  17_guess - 17_high  Left       face     0.9276422 0.1460076 29514.59     -Inf 1.2772015   2.929  1.0000
##  17_guess - 200_high Left       face     1.2904477 0.1469225 38302.54     -Inf 1.6421927   5.380  1.0000
##  17_low - 17_high    Left       face     0.2517803 0.1169049 27206.64     -Inf 0.5316656  -2.123  0.1012
##  17_low - 200_high   Left       face     0.6145858 0.1203932 38772.85     -Inf 0.9028171   0.952  1.0000
##  17_high - 200_high  Left       face     0.3628055 0.1131308 38093.91     -Inf 0.6336502  -1.213  0.6757
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests 
## Statistics are tests of nonsuperiority with a threshold of 0.5 
## P values are left-tailed

Comparisons of N170 amplitudes for (intact) faces with different durations and confidence (right hemisphere):

contra_SC_N170[7:12]

##  contrast            Hemisphere Category  estimate        SE       df  lower.CL  upper.CL t.ratio p.value
##  17_guess - 17_low   Right      face     0.8155917 0.1393891 37587.07 0.4478279 1.1833556   5.851  <.0001
##  17_guess - 17_high  Right      face     1.2764073 0.1454449 22943.21 0.8926529 1.6601616   8.776  <.0001
##  17_guess - 200_high Right      face     1.9407591 0.1467338 36887.28 1.5536167 2.3279014  13.226  <.0001
##  17_low - 17_high    Right      face     0.4608155 0.1163863 20095.74 0.1537282 0.7679028   3.959  0.0005
##  17_low - 200_high   Right      face     1.1251673 0.1202709 37878.28 0.8078451 1.4424896   9.355  <.0001
##  17_high - 200_high  Right      face     0.6643518 0.1129761 36540.10 0.3662755 0.9624281   5.880  <.0001
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests

summary(contra_SC_N170[7:12], delta = delta_null, level = .9)

##  contrast            Hemisphere Category  estimate        SE       df  lower.CL  upper.CL t.ratio p.value
##  17_guess - 17_low   Right      face     0.8155917 0.1393891 37587.07 0.4818820 1.1493014   2.264  1.0000
##  17_guess - 17_high  Right      face     1.2764073 0.1454449 22943.21 0.9281896 1.6246249   5.338  1.0000
##  17_guess - 200_high Right      face     1.9407591 0.1467338 36887.28 1.5894653 2.2920528   9.819  1.0000
##  17_low - 17_high    Right      face     0.4608155 0.1163863 20095.74 0.1821658 0.7394652  -0.337  1.0000
##  17_low - 200_high   Right      face     1.1251673 0.1202709 37878.28 0.8372285 1.4131062   5.198  1.0000
##  17_high - 200_high  Right      face     0.6643518 0.1129761 36540.10 0.3938768 0.9348268   1.455  1.0000
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

summary(contra_SC_N170[7:12], delta = delta_null, side = ">")

##  contrast            Hemisphere Category  estimate        SE       df  lower.CL upper.CL t.ratio p.value
##  17_guess - 17_low   Right      face     0.8155917 0.1393891 37587.07 0.4818820      Inf   9.438  <.0001
##  17_guess - 17_high  Right      face     1.2764073 0.1454449 22943.21 0.9281896      Inf  12.214  <.0001
##  17_guess - 200_high Right      face     1.9407591 0.1467338 36887.28 1.5894653      Inf  16.634  <.0001
##  17_low - 17_high    Right      face     0.4608155 0.1163863 20095.74 0.1821658      Inf   8.255  <.0001
##  17_low - 200_high   Right      face     1.1251673 0.1202709 37878.28 0.8372285      Inf  13.513  <.0001
##  17_high - 200_high  Right      face     0.6643518 0.1129761 36540.10 0.3938768      Inf  10.306  <.0001
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests 
## Statistics are tests of noninferiority with a threshold of 0.5 
## P values are right-tailed

summary(contra_SC_N170[7:12], delta = delta_null, side = "<")

##  contrast            Hemisphere Category  estimate        SE       df lower.CL  upper.CL t.ratio p.value
##  17_guess - 17_low   Right      face     0.8155917 0.1393891 37587.07     -Inf 1.1493014   2.264  1.0000
##  17_guess - 17_high  Right      face     1.2764073 0.1454449 22943.21     -Inf 1.6246249   5.338  1.0000
##  17_guess - 200_high Right      face     1.9407591 0.1467338 36887.28     -Inf 2.2920528   9.819  1.0000
##  17_low - 17_high    Right      face     0.4608155 0.1163863 20095.74     -Inf 0.7394652  -0.337  1.0000
##  17_low - 200_high   Right      face     1.1251673 0.1202709 37878.28     -Inf 1.4131062   5.198  1.0000
##  17_high - 200_high  Right      face     0.6643518 0.1129761 36540.10     -Inf 0.9348268   1.455  1.0000
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests 
## Statistics are tests of nonsuperiority with a threshold of 0.5 
## P values are left-tailed

Comparisons of N170 amplitude lateralization for (intact) faces with different durations and confidence:

contra_SC_N170_hemi <- contrast(emmeans(lmm.opt.N170.SC.E205, ~ DuraConf + Hemisphere | Category), # 
                                    interaction = "pairwise", adjust = "Bonferroni", infer = TRUE)
contra_SC_N170_hemi[1:6]

##  DuraConf_pairwise   Hemisphere_pairwise Category   estimate        SE       df   lower.CL   upper.CL t.ratio p.value
##  17_guess - 17_low   Left - Right        face     -0.1397298 0.1969420 37121.29 -0.6593412  0.3798816  -0.709  1.0000
##  17_guess - 17_high  Left - Right        face     -0.3487650 0.2052731 21129.07 -0.8903792  0.1928491  -1.699  0.5360
##  17_guess - 200_high Left - Right        face     -0.6503113 0.2073552 36333.86 -1.1973977 -0.1032250  -3.136  0.0103
##  17_low - 17_high    Left - Right        face     -0.2090352 0.1642874 18263.34 -0.6425150  0.2244445  -1.272  1.0000
##  17_low - 200_high   Left - Right        face     -0.5105815 0.1700071 37522.46 -0.9591277 -0.0620354  -3.003  0.0160
##  17_high - 200_high  Left - Right        face     -0.3015463 0.1596700 35954.30 -0.7228200  0.1197275  -1.889  0.3538
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests

summary(contra_SC_N170_hemi[1:6], delta = delta_null, level = .9)

##  DuraConf_pairwise   Hemisphere_pairwise Category   estimate        SE       df   lower.CL   upper.CL t.ratio p.value
##  17_guess - 17_low   Left - Right        face     -0.1397298 0.1969420 37121.29 -0.6112262  0.3317666  -1.829  0.2021
##  17_guess - 17_high  Left - Right        face     -0.3487650 0.2052731 21129.07 -0.8402238  0.1426937  -0.737  1.0000
##  17_guess - 200_high Left - Right        face     -0.6503113 0.2073552 36333.86 -1.1467385 -0.1538841   0.725  1.0000
##  17_low - 17_high    Left - Right        face     -0.2090352 0.1642874 18263.34 -0.6023723  0.1843018  -1.771  0.2297
##  17_low - 200_high   Left - Right        face     -0.5105815 0.1700071 37522.46 -0.9175933 -0.1035698   0.062  1.0000
##  17_high - 200_high  Left - Right        face     -0.3015463 0.1596700 35954.30 -0.6838108  0.0807183  -1.243  0.6417
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

summary(contra_SC_N170_hemi[1:6], delta = delta_null, side = ">")

##  DuraConf_pairwise   Hemisphere_pairwise Category   estimate        SE       df   lower.CL upper.CL t.ratio p.value
##  17_guess - 17_low   Left - Right        face     -0.1397298 0.1969420 37121.29 -0.6112262      Inf   1.829  0.2021
##  17_guess - 17_high  Left - Right        face     -0.3487650 0.2052731 21129.07 -0.8402238      Inf   0.737  1.0000
##  17_guess - 200_high Left - Right        face     -0.6503113 0.2073552 36333.86 -1.1467385      Inf  -0.725  1.0000
##  17_low - 17_high    Left - Right        face     -0.2090352 0.1642874 18263.34 -0.6023723      Inf   1.771  0.2297
##  17_low - 200_high   Left - Right        face     -0.5105815 0.1700071 37522.46 -0.9175933      Inf  -0.062  1.0000
##  17_high - 200_high  Left - Right        face     -0.3015463 0.1596700 35954.30 -0.6838108      Inf   1.243  0.6417
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests 
## Statistics are tests of noninferiority with a threshold of 0.5 
## P values are right-tailed

summary(contra_SC_N170_hemi[1:6], delta = delta_null, side = "<")

##  DuraConf_pairwise   Hemisphere_pairwise Category   estimate        SE       df lower.CL   upper.CL t.ratio p.value
##  17_guess - 17_low   Left - Right        face     -0.1397298 0.1969420 37121.29     -Inf  0.3317666  -3.248  0.0035
##  17_guess - 17_high  Left - Right        face     -0.3487650 0.2052731 21129.07     -Inf  0.1426937  -4.135  0.0001
##  17_guess - 200_high Left - Right        face     -0.6503113 0.2073552 36333.86     -Inf -0.1538841  -5.548  <.0001
##  17_low - 17_high    Left - Right        face     -0.2090352 0.1642874 18263.34     -Inf  0.1843018  -4.316  <.0001
##  17_low - 200_high   Left - Right        face     -0.5105815 0.1700071 37522.46     -Inf -0.1035698  -5.944  <.0001
##  17_high - 200_high  Left - Right        face     -0.3015463 0.1596700 35954.30     -Inf  0.0807183  -5.020  <.0001
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests 
## Statistics are tests of nonsuperiority with a threshold of 0.5 
## P values are left-tailed

Comparisons of face-specific N170 amplitude components for different durations and confidence (left hemisphere):

contra_SC_N170_spec <- contrast(emmeans(lmm.opt.N170.SC.E205, ~ Category + DuraConf | Hemisphere), # 
                                interaction = "pairwise", adjust = "Bonferroni", infer = TRUE)

contra_SC_N170_spec[1:6]

##  Category_pairwise DuraConf_pairwise   Hemisphere  estimate        SE       df   lower.CL  upper.CL t.ratio p.value
##  face - house      17_guess - 17_low   Left       0.3676071 0.1758644 29292.96 -0.0963998 0.8316141   2.090  0.2196
##  face - house      17_guess - 17_high  Left       0.7486509 0.2128594  8673.02  0.1869443 1.3103575   3.517  0.0026
##  face - house      17_guess - 200_high Left       2.3845078 0.1868966 34616.71  1.8913980 2.8776175  12.758  <.0001
##  face - house      17_low - 17_high    Left       0.3810437 0.1874597 11931.55 -0.1136056 0.8756931   2.033  0.2526
##  face - house      17_low - 200_high   Left       2.0169006 0.1659301 38713.14  1.5791118 2.4546895  12.155  <.0001
##  face - house      17_high - 200_high  Left       1.6358569 0.1919169 19722.82  1.1294796 2.1422342   8.524  <.0001
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests

summary(contra_SC_N170_spec[1:6], delta = delta_null, level = .9)

##  Category_pairwise DuraConf_pairwise   Hemisphere  estimate        SE       df   lower.CL  upper.CL t.ratio p.value
##  face - house      17_guess - 17_low   Left       0.3676071 0.1758644 29292.96 -0.0534328 0.7886470  -0.753  1.0000
##  face - house      17_guess - 17_high  Left       0.7486509 0.2128594  8673.02  0.2389710 1.2583308   1.168  1.0000
##  face - house      17_guess - 200_high Left       2.3845078 0.1868966 34616.71  1.9370593 2.8319563  10.083  1.0000
##  face - house      17_low - 17_high    Left       0.3810437 0.1874597 11931.55 -0.0677943 0.8298818  -0.635  1.0000
##  face - house      17_low - 200_high   Left       2.0169006 0.1659301 38713.14  1.6196500 2.4141513   9.142  1.0000
##  face - house      17_high - 200_high  Left       1.6358569 0.1919169 19722.82  1.1763725 2.0953413   5.918  1.0000
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

summary(contra_SC_N170_spec[1:6], delta = delta_null, side = ">")

##  Category_pairwise DuraConf_pairwise   Hemisphere  estimate        SE       df   lower.CL upper.CL t.ratio p.value
##  face - house      17_guess - 17_low   Left       0.3676071 0.1758644 29292.96 -0.0534328      Inf   4.933  <.0001
##  face - house      17_guess - 17_high  Left       0.7486509 0.2128594  8673.02  0.2389710      Inf   5.866  <.0001
##  face - house      17_guess - 200_high Left       2.3845078 0.1868966 34616.71  1.9370593      Inf  15.434  <.0001
##  face - house      17_low - 17_high    Left       0.3810437 0.1874597 11931.55 -0.0677943      Inf   4.700  <.0001
##  face - house      17_low - 200_high   Left       2.0169006 0.1659301 38713.14  1.6196500      Inf  15.168  <.0001
##  face - house      17_high - 200_high  Left       1.6358569 0.1919169 19722.82  1.1763725      Inf  11.129  <.0001
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests 
## Statistics are tests of noninferiority with a threshold of 0.5 
## P values are right-tailed

summary(contra_SC_N170_spec[1:6], delta = delta_null, side = "<")

##  Category_pairwise DuraConf_pairwise   Hemisphere  estimate        SE       df lower.CL  upper.CL t.ratio p.value
##  face - house      17_guess - 17_low   Left       0.3676071 0.1758644 29292.96     -Inf 0.7886470  -0.753  1.0000
##  face - house      17_guess - 17_high  Left       0.7486509 0.2128594  8673.02     -Inf 1.2583308   1.168  1.0000
##  face - house      17_guess - 200_high Left       2.3845078 0.1868966 34616.71     -Inf 2.8319563  10.083  1.0000
##  face - house      17_low - 17_high    Left       0.3810437 0.1874597 11931.55     -Inf 0.8298818  -0.635  1.0000
##  face - house      17_low - 200_high   Left       2.0169006 0.1659301 38713.14     -Inf 2.4141513   9.142  1.0000
##  face - house      17_high - 200_high  Left       1.6358569 0.1919169 19722.82     -Inf 2.0953413   5.918  1.0000
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests 
## Statistics are tests of nonsuperiority with a threshold of 0.5 
## P values are left-tailed

Comparisons of face-specific N170 amplitude components for different durations and confidence (right hemisphere):

contra_SC_N170_spec[7:12]

##  Category_pairwise DuraConf_pairwise   Hemisphere  estimate        SE       df   lower.CL upper.CL t.ratio p.value
##  face - house      17_guess - 17_low   Right      0.6408724 0.1749512 20554.18  0.1792613 1.102483   3.663  0.0015
##  face - house      17_guess - 17_high  Right      0.9632012 0.2094179  3906.37  0.4104213 1.515981   4.599  <.0001
##  face - house      17_guess - 200_high Right      2.8676323 0.1862807 29077.11  2.3761421 3.359122  15.394  <.0001
##  face - house      17_low - 17_high    Right      0.3223288 0.1850160  5675.08 -0.1659622 0.810620   1.742  0.4892
##  face - house      17_low - 200_high   Right      2.2267599 0.1657007 37310.22  1.7895756 2.663944  13.438  <.0001
##  face - house      17_high - 200_high  Right      1.9044311 0.1902513 11186.98  1.4024099 2.406452  10.010  <.0001
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests

summary(contra_SC_N170_spec[7:12], delta = delta_null, level = .9)

##  Category_pairwise DuraConf_pairwise   Hemisphere  estimate        SE       df   lower.CL upper.CL t.ratio p.value
##  face - house      17_guess - 17_low   Right      0.6408724 0.1749512 20554.18  0.2220084 1.059736   0.805  1.0000
##  face - house      17_guess - 17_high  Right      0.9632012 0.2094179  3906.37  0.4616429 1.464759   2.212  1.0000
##  face - house      17_guess - 200_high Right      2.8676323 0.1862807 29077.11  2.4216541 3.313610  12.710  1.0000
##  face - house      17_low - 17_high    Right      0.3223288 0.1850160  5675.08 -0.1207271 0.765385  -0.960  1.0000
##  face - house      17_low - 200_high   Right      2.2267599 0.1657007 37310.22  1.8300580 2.623462  10.421  1.0000
##  face - house      17_high - 200_high  Right      1.9044311 0.1902513 11186.98  1.4489048 2.359957   7.382  1.0000
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

summary(contra_SC_N170_spec[7:12], delta = delta_null, side = ">")

##  Category_pairwise DuraConf_pairwise   Hemisphere  estimate        SE       df   lower.CL upper.CL t.ratio p.value
##  face - house      17_guess - 17_low   Right      0.6408724 0.1749512 20554.18  0.2220084      Inf   6.521  <.0001
##  face - house      17_guess - 17_high  Right      0.9632012 0.2094179  3906.37  0.4616429      Inf   6.987  <.0001
##  face - house      17_guess - 200_high Right      2.8676323 0.1862807 29077.11  2.4216541      Inf  18.078  <.0001
##  face - house      17_low - 17_high    Right      0.3223288 0.1850160  5675.08 -0.1207271      Inf   4.445  <.0001
##  face - house      17_low - 200_high   Right      2.2267599 0.1657007 37310.22  1.8300580      Inf  16.456  <.0001
##  face - house      17_high - 200_high  Right      1.9044311 0.1902513 11186.98  1.4489048      Inf  12.638  <.0001
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests 
## Statistics are tests of noninferiority with a threshold of 0.5 
## P values are right-tailed

summary(contra_SC_N170_spec[7:12], delta = delta_null, side = "<")

##  Category_pairwise DuraConf_pairwise   Hemisphere  estimate        SE       df lower.CL upper.CL t.ratio p.value
##  face - house      17_guess - 17_low   Right      0.6408724 0.1749512 20554.18     -Inf 1.059736   0.805  1.0000
##  face - house      17_guess - 17_high  Right      0.9632012 0.2094179  3906.37     -Inf 1.464759   2.212  1.0000
##  face - house      17_guess - 200_high Right      2.8676323 0.1862807 29077.11     -Inf 3.313610  12.710  1.0000
##  face - house      17_low - 17_high    Right      0.3223288 0.1850160  5675.08     -Inf 0.765385  -0.960  1.0000
##  face - house      17_low - 200_high   Right      2.2267599 0.1657007 37310.22     -Inf 2.623462  10.421  1.0000
##  face - house      17_high - 200_high  Right      1.9044311 0.1902513 11186.98     -Inf 2.359957   7.382  1.0000
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests 
## Statistics are tests of nonsuperiority with a threshold of 0.5 
## P values are left-tailed

Comparisons of face-specific N170 amplitude component lateralization for different durations and confidence:

contra_SC_N170_spec_hemi <- contrast(emmeans(lmm.opt.N170.SC.E205, ~ Category + DuraConf + Hemisphere), # 
                                interaction = "pairwise", adjust = "Bonferroni", infer = TRUE)

contra_SC_N170_spec_hemi

##  Category_pairwise DuraConf_pairwise   Hemisphere_pairwise   estimate        SE       df   lower.CL  upper.CL t.ratio p.value
##  face - house      17_guess - 17_low   Left - Right        -0.2732653 0.2464975 17365.60 -0.9236637 0.3771332  -1.109  1.0000
##  face - house      17_guess - 17_high  Left - Right        -0.2145503 0.2931618  2919.39 -0.9885142 0.5594137  -0.732  1.0000
##  face - house      17_guess - 200_high Left - Right        -0.4831245 0.2629621 27102.35 -1.1769371 0.2106881  -1.837  0.3971
##  face - house      17_low - 17_high    Left - Right         0.0587150 0.2597280  4296.86 -0.6268319 0.7442619   0.226  1.0000
##  face - house      17_low - 200_high   Left - Right        -0.2098592 0.2341167 36563.69 -0.8275529 0.4078344  -0.896  1.0000
##  face - house      17_high - 200_high  Left - Right        -0.2685742 0.2674890  8591.17 -0.9744424 0.4372940  -1.004  1.0000
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests

summary(contra_SC_N170_spec_hemi, delta = delta_null, level = .9)

##  Category_pairwise DuraConf_pairwise   Hemisphere_pairwise   estimate        SE       df   lower.CL  upper.CL t.ratio p.value
##  face - house      17_guess - 17_low   Left - Right        -0.2732653 0.2464975 17365.60 -0.8634325 0.3169020  -0.920  1.0000
##  face - house      17_guess - 17_high  Left - Right        -0.2145503 0.2931618  2919.39 -0.9167786 0.4876780  -0.974  0.9909
##  face - house      17_guess - 200_high Left - Right        -0.4831245 0.2629621 27102.35 -1.1126895 0.1464405  -0.064  1.0000
##  face - house      17_low - 17_high    Left - Right         0.0587150 0.2597280  4296.86 -0.5633123 0.6807423  -1.699  0.2682
##  face - house      17_low - 200_high   Left - Right        -0.2098592 0.2341167 36563.69 -0.7703557 0.3506372  -1.239  0.6457
##  face - house      17_high - 200_high  Left - Right        -0.2685742 0.2674890  8591.17 -0.9090628 0.3719144  -0.865  1.0000
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

summary(contra_SC_N170_spec_hemi, delta = delta_null, side = ">")

##  Category_pairwise DuraConf_pairwise   Hemisphere_pairwise   estimate        SE       df   lower.CL upper.CL t.ratio p.value
##  face - house      17_guess - 17_low   Left - Right        -0.2732653 0.2464975 17365.60 -0.8634325      Inf   0.920  1.0000
##  face - house      17_guess - 17_high  Left - Right        -0.2145503 0.2931618  2919.39 -0.9167786      Inf   0.974  0.9909
##  face - house      17_guess - 200_high Left - Right        -0.4831245 0.2629621 27102.35 -1.1126895      Inf   0.064  1.0000
##  face - house      17_low - 17_high    Left - Right         0.0587150 0.2597280  4296.86 -0.5633123      Inf   2.151  0.0946
##  face - house      17_low - 200_high   Left - Right        -0.2098592 0.2341167 36563.69 -0.7703557      Inf   1.239  0.6457
##  face - house      17_high - 200_high  Left - Right        -0.2685742 0.2674890  8591.17 -0.9090628      Inf   0.865  1.0000
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests 
## Statistics are tests of noninferiority with a threshold of 0.5 
## P values are right-tailed

summary(contra_SC_N170_spec_hemi, delta = delta_null, side = "<")

##  Category_pairwise DuraConf_pairwise   Hemisphere_pairwise   estimate        SE       df lower.CL  upper.CL t.ratio p.value
##  face - house      17_guess - 17_low   Left - Right        -0.2732653 0.2464975 17365.60     -Inf 0.3169020  -3.137  0.0051
##  face - house      17_guess - 17_high  Left - Right        -0.2145503 0.2931618  2919.39     -Inf 0.4876780  -2.437  0.0446
##  face - house      17_guess - 200_high Left - Right        -0.4831245 0.2629621 27102.35     -Inf 0.1464405  -3.739  0.0006
##  face - house      17_low - 17_high    Left - Right         0.0587150 0.2597280  4296.86     -Inf 0.6807423  -1.699  0.2682
##  face - house      17_low - 200_high   Left - Right        -0.2098592 0.2341167 36563.69     -Inf 0.3506372  -3.032  0.0073
##  face - house      17_high - 200_high  Left - Right        -0.2685742 0.2674890  8591.17     -Inf 0.3719144  -2.873  0.0122
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 6 estimates 
## P value adjustment: bonferroni method for 6 tests 
## Statistics are tests of nonsuperiority with a threshold of 0.5 
## P values are left-tailed

5.3.4.9.2 Other results

Comparisons of N170 amplitudes for different durations and confidence (without multiple comparison corrections):

summary(contra_SC_N170[1:24], adjust = "none")

##  contrast            Hemisphere Category   estimate        SE       df   lower.CL   upper.CL t.ratio p.value
##  17_guess - 17_low   Left       face      0.6758619 0.1395425 38713.15  0.4023552  0.9493687   4.843  <.0001
##  17_guess - 17_high  Left       face      0.9276422 0.1460076 29514.59  0.6414608  1.2138236   6.353  <.0001
##  17_guess - 200_high Left       face      1.2904477 0.1469225 38302.54  1.0024758  1.5784197   8.783  <.0001
##  17_low - 17_high    Left       face      0.2517803 0.1169049 27206.64  0.0226407  0.4809199   2.154  0.0313
##  17_low - 200_high   Left       face      0.6145858 0.1203932 38772.85  0.3786122  0.8505594   5.105  <.0001
##  17_high - 200_high  Left       face      0.3628055 0.1131308 38093.91  0.1410663  0.5845448   3.207  0.0013
##  17_guess - 17_low   Right      face      0.8155917 0.1393891 37587.07  0.5423852  1.0887982   5.851  <.0001
##  17_guess - 17_high  Right      face      1.2764073 0.1454449 22943.21  0.9913255  1.5614890   8.776  <.0001
##  17_guess - 200_high Right      face      1.9407591 0.1467338 36887.28  1.6531567  2.2283614  13.226  <.0001
##  17_low - 17_high    Right      face      0.4608155 0.1163863 20095.74  0.2326889  0.6889421   3.959  0.0001
##  17_low - 200_high   Right      face      1.1251673 0.1202709 37878.28  0.8894332  1.3609015   9.355  <.0001
##  17_high - 200_high  Right      face      0.6643518 0.1129761 36540.10  0.4429154  0.8857882   5.880  <.0001
##  17_guess - 17_low   Left       house     0.3082548 0.1076292 20078.08  0.0972927  0.5192169   2.864  0.0042
##  17_guess - 17_high  Left       house     0.1789913 0.1573651  8525.69 -0.1294823  0.4874650   1.137  0.2554
##  17_guess - 200_high Left       house    -1.0940600 0.1161555 35903.71 -1.3217282 -0.8663918  -9.419  <.0001
##  17_low - 17_high    Left       house    -0.1292635 0.1480476 13810.14 -0.4194568  0.1609299  -0.873  0.3826
##  17_low - 200_high   Left       house    -1.4023148 0.1141830 38043.44 -1.6261165 -1.1785132 -12.281  <.0001
##  17_high - 200_high  Left       house    -1.2730514 0.1558475 18473.29 -1.5785269 -0.9675758  -8.169  <.0001
##  17_guess - 17_low   Right      house     0.1747193 0.1067423 11408.76 -0.0345140  0.3839527   1.637  0.1017
##  17_guess - 17_high  Right      house     0.3132061 0.1548649  3900.54  0.0095823  0.6168299   2.022  0.0432
##  17_guess - 200_high Right      house    -0.9268732 0.1158291 31476.88 -1.1539028 -0.6998436  -8.002  <.0001
##  17_low - 17_high    Right      house     0.1384868 0.1463593  6882.61 -0.1484227  0.4253962   0.946  0.3441
##  17_low - 200_high   Right      house    -1.1015926 0.1139792 35765.35 -1.3249953 -0.8781898  -9.665  <.0001
##  17_high - 200_high  Right      house    -1.2400793 0.1544484 10310.52 -1.5428282 -0.9373304  -8.029  <.0001
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95

summary(contra_SC_N170[1:24], adjust = "none", delta = delta_null, level = .9)

##  contrast            Hemisphere Category   estimate        SE       df   lower.CL   upper.CL t.ratio p.value
##  17_guess - 17_low   Left       face      0.6758619 0.1395425 38713.15  0.4463295  0.9053944   1.260  0.8962
##  17_guess - 17_high  Left       face      0.9276422 0.1460076 29514.59  0.6874735  1.1678109   2.929  0.9983
##  17_guess - 200_high Left       face      1.2904477 0.1469225 38302.54  1.0487759  1.5321196   5.380  1.0000
##  17_low - 17_high    Left       face      0.2517803 0.1169049 27206.64  0.0594823  0.4440783  -2.123  0.0169
##  17_low - 200_high   Left       face      0.6145858 0.1203932 38772.85  0.4165520  0.8126196   0.952  0.8294
##  17_high - 200_high  Left       face      0.3628055 0.1131308 38093.91  0.1767175  0.5488936  -1.213  0.1126
##  17_guess - 17_low   Right      face      0.8155917 0.1393891 37587.07  0.5863114  1.0448721   2.264  0.9882
##  17_guess - 17_high  Right      face      1.2764073 0.1454449 22943.21  1.0371620  1.5156525   5.338  1.0000
##  17_guess - 200_high Right      face      1.9407591 0.1467338 36887.28  1.6993974  2.1821207   9.819  1.0000
##  17_low - 17_high    Right      face      0.4608155 0.1163863 20095.74  0.2693683  0.6522627  -0.337  0.3682
##  17_low - 200_high   Right      face      1.1251673 0.1202709 37878.28  0.9273345  1.3230002   5.198  1.0000
##  17_high - 200_high  Right      face      0.6643518 0.1129761 36540.10  0.4785179  0.8501857   1.455  0.9271
##  17_guess - 17_low   Left       house     0.3082548 0.1076292 20078.08  0.1312123  0.4852973  -1.782  0.0374
##  17_guess - 17_high  Left       house     0.1789913 0.1573651  8525.69 -0.0798793  0.4378619  -2.040  0.0207
##  17_guess - 200_high Left       house    -1.0940600 0.1161555 35903.71 -1.2851237 -0.9029964   5.114  1.0000
##  17_low - 17_high    Left       house    -0.1292635 0.1480476 13810.14 -0.3727964  0.1142695  -2.504  0.0061
##  17_low - 200_high   Left       house    -1.4023148 0.1141830 38043.44 -1.5901337 -1.2144960   7.902  1.0000
##  17_high - 200_high  Left       house    -1.2730514 0.1558475 18473.29 -1.5294106 -1.0166921   4.960  1.0000
##  17_guess - 17_low   Right      house     0.1747193 0.1067423 11408.76 -0.0008704  0.3503091  -3.047  0.0012
##  17_guess - 17_high  Right      house     0.3132061 0.1548649  3900.54  0.0584155  0.5679967  -1.206  0.1139
##  17_guess - 200_high Right      house    -0.9268732 0.1158291 31476.88 -1.1174007 -0.7363457   3.685  0.9999
##  17_low - 17_high    Right      house     0.1384868 0.1463593  6882.61 -0.1022853  0.3792588  -2.470  0.0068
##  17_low - 200_high   Right      house    -1.1015926 0.1139792 35765.35 -1.2890765 -0.9141086   5.278  1.0000
##  17_high - 200_high  Right      house    -1.2400793 0.1544484 10310.52 -1.4941472 -0.9860114   4.792  1.0000
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

5.4 Linear mixed model with high subjective confidences

tmpBlock.count <- {
  clean_beha_205 %>% 
    filter(Type == "intact") %>% 
    group_by(SubjCode, Category, Duration, blockID) %>% 
    summarize(Count_sum = n())
}

avg.resp.long.E205.Block <- {
  clean_beha_205 %>% 
    filter(Type == "intact") %>% 
    group_by(SubjCode, Category, Duration, blockID, Resp) %>% 
    summarize(Count = n()) %>% 
    
    right_join(tmpBlock.count, by = c("SubjCode", "blockID", "Category", "Duration")) %>% 
    mutate(RespRate = Count / Count_sum) %>% 
    ungroup()
}

# descriptive statistics of accuracy for plotting
desc.resp.E205.Block <- {
  avg.resp.long.E205.Block %>%
    group_by(blockID, Category, Duration, Resp) %>%
    summarize(Mean = mean(RespRate)
              , N = n()
              , SD = if_else(N > 1, sd(RespRate), 0)
              , SE = SD/sqrt(N)
              , SE.lo = Mean - SE, SE.hi = Mean + SE
              , CI = SE * qt(0.975,N)
              , Median = median(RespRate)
              , Lower = Mean - SD, Upper = Mean + SD # for rainclound plot
    ) %>% 
    ungroup() %>% 
    add_row(blockID = "2", Category = "house", Duration = "200", Resp = "2", Mean = 0, N = 0, SD = 0, SE = 0, SE.lo = 0,
            SE.hi = 0, CI = 0, Median = 0, Lower = 0, Upper = 0)
}

avg.resp.wide.E205.Block <- {
  avg.resp.long.E205.Block %>% 
    mutate(Conditions = paste(blockID, Category, Duration, Resp, sep = "_")) %>% 
    select(SubjCode, Conditions, RespRate) %>% 
    spread(Conditions, RespRate)
}

block.high <- {
  avg.resp.long.E205.Block %>% 
    filter(Category == "face", Duration == 17, Resp == 1, SubjCode != "P513") 
}

t.test(filter(block.high, blockID == 1)$RespRate, filter(block.high, blockID == 2)$RespRate, paired = T)

## 
##  Paired t-test
## 
## data:  filter(block.high, blockID == 1)$RespRate and filter(block.high, blockID == 2)$RespRate
## t = -0.64323, df = 26, p-value = 0.5257
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.10424318  0.05455182
## sample estimates:
## mean of the differences 
##             -0.02484568

knitr::kable(desc.resp.E205.Block, digits = 4)

blockID	Category	Duration	Resp	Mean	N	SD	SE	SE.lo	SE.hi	CI	Median	Lower	Upper
1	face	17	1	0.4628	28	0.3419	0.0646	0.3982	0.5274	0.1324	0.3812	0.1209	0.8047
1	face	17	2	0.3192	28	0.2240	0.0423	0.2769	0.3615	0.0867	0.3354	0.0952	0.5432
1	face	17	3	0.1737	28	0.1856	0.0351	0.1386	0.2087	0.0719	0.0958	-0.0120	0.3593
1	face	17	4	0.0472	25	0.0638	0.0128	0.0344	0.0599	0.0263	0.0208	-0.0166	0.1109
1	face	17	5	0.0156	4	0.0202	0.0101	0.0055	0.0257	0.0281	0.0063	-0.0046	0.0359
1	house	17	1	0.0120	17	0.0107	0.0026	0.0094	0.0146	0.0055	0.0083	0.0013	0.0227
1	house	17	2	0.0658	28	0.0806	0.0152	0.0505	0.0810	0.0312	0.0417	-0.0148	0.1464
1	house	17	3	0.4188	28	0.2770	0.0524	0.3664	0.4711	0.1072	0.3542	0.1417	0.6958
1	house	17	4	0.3890	27	0.2334	0.0449	0.3441	0.4340	0.0922	0.4167	0.1556	0.6225
1	house	17	5	0.1961	19	0.2386	0.0547	0.1413	0.2508	0.1146	0.1042	-0.0425	0.4347
2	face	17	1	0.5046	27	0.3326	0.0640	0.4406	0.5686	0.1313	0.4750	0.1721	0.8372
2	face	17	2	0.3786	24	0.2660	0.0543	0.3243	0.4329	0.1121	0.3875	0.1126	0.6446
2	face	17	3	0.1300	25	0.1197	0.0239	0.1061	0.1539	0.0493	0.0750	0.0103	0.2497
2	face	17	4	0.0863	21	0.0828	0.0181	0.0682	0.1044	0.0376	0.0500	0.0035	0.1691
2	face	17	5	0.0225	10	0.0184	0.0058	0.0167	0.0283	0.0130	0.0125	0.0041	0.0409
2	face	200	1	0.9768	28	0.0320	0.0061	0.9707	0.9828	0.0124	0.9875	0.9447	1.0088
2	face	200	2	0.0400	10	0.0403	0.0127	0.0273	0.0527	0.0284	0.0188	-0.0003	0.0803
2	face	200	3	0.0208	9	0.0125	0.0042	0.0167	0.0250	0.0094	0.0125	0.0083	0.0333
2	face	200	4	0.0125	1	0.0000	0.0000	0.0125	0.0125	0.0000	0.0125	0.0125	0.0125
2	face	200	5	0.0125	4	0.0000	0.0000	0.0125	0.0125	0.0000	0.0125	0.0125	0.0125
2	house	17	1	0.0156	4	0.0063	0.0031	0.0125	0.0187	0.0087	0.0125	0.0094	0.0219
2	house	17	2	0.0354	18	0.0412	0.0097	0.0257	0.0451	0.0204	0.0250	-0.0058	0.0766
2	house	17	3	0.3032	27	0.2482	0.0478	0.2555	0.3510	0.0980	0.3000	0.0550	0.5515
2	house	17	4	0.4696	28	0.2377	0.0449	0.4247	0.5146	0.0920	0.4563	0.2319	0.7074
2	house	17	5	0.2208	27	0.2831	0.0545	0.1664	0.2753	0.1118	0.0750	-0.0622	0.5039
2	house	200	1	0.0187	6	0.0105	0.0043	0.0145	0.0230	0.0104	0.0125	0.0083	0.0292
2	house	200	3	0.0150	10	0.0079	0.0025	0.0125	0.0175	0.0056	0.0125	0.0071	0.0229
2	house	200	4	0.0331	20	0.0208	0.0046	0.0285	0.0378	0.0097	0.0250	0.0123	0.0539
2	house	200	5	0.9670	28	0.0262	0.0049	0.9620	0.9719	0.0101	0.9750	0.9408	0.9931
2	house	200	2	0.0000	0	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000

rt.RainPlot.E205.Block <- {
  ggplot(data = avg.resp.long.E205.Block, aes(y = RespRate, x = as.factor(Resp), fill = Category)) + 
    geom_flat_violin(position = position_nudge(x = .2, y = 0), alpha = .7) +
    geom_point(aes(y = RespRate, color = Category), position = position_jitter(width = .15), size = .5, alpha = .8) +
    geom_boxplot(aes(y = RespRate), width = .2, outlier.shape = NA, alpha = .7) +
    geom_point(data = desc.resp.E205.Block, aes(y = Mean, x = Resp, color = Category), position = position_nudge(x = 0.3), size = 2.5) +
    geom_errorbar(data = desc.resp.E205.Block, aes(y = Mean, ymin = Lower, ymax = Upper), position = position_nudge(x = 0.3), width = 0) +
    facet_grid(Duration ~ blockID) +
    
    # facet_grid(. ~ Type, switch = "x") +  # create two panels to show data
    # scale_colour_grey() + # start = .1, end = .6, color for the contour
    # scale_fill_grey() + # start = .3, end = .6, color for the fill
    # scale_color_brewer(palette = "Set1") + # palette = "RdBu"
    # scale_fill_brewer(palette = "Set1") + # palette = "RdBu"
    labs(title = "Reaction times for E205", x = "Stimulus Type", y = "Reaction times (ms)", fill = "Duration(ms)", color = "Duration(ms)") +  # set the names for main, x and y axises and the legend
    # coord_cartesian(ylim = c(0, 1.05)) +  # set the limit for y axis
    # scale_y_continuous(expand= c(0, 0)) +  # remove the space between columns and x axis
    theme_bw() + # the backgroud color
    plot_theme +
    NULL
}

 

rt.RainPlot.E205.Block

bloc.labels <- c("1st Half", "2nd Half")
names(bloc.labels) <- c(1, 2)

acc.ColuPlot.E205.Block <- {
  ggplot(data = desc.resp.E205.Block, aes(y = Mean, x = Resp, fill = Category)) +  # set the data, varialbes for x and y axises, and the variable for dividing data
    geom_col(position = "dodge", color = "black", alpha = .7) +  # position of columns and countour of columns
    facet_grid(Duration ~ blockID,
               labeller = labeller(Duration = dura.labels,
                                   blockID = bloc.labels)) +
    geom_errorbar(mapping = aes(ymin = SE.lo, ymax = SE.hi), linetype = 1,  # set the error bar
                  show.legend = FALSE, width = 0.25, alpha = .5,
                  position = position_dodge(width=0.9)) +
    geom_hline(yintercept = c(1), linetype = 5, alpha = 0.5) +  # add the line for 0.5 and 1 (y)
    coord_cartesian(ylim = c(0,1.1)) +  # set the limit for y axis
    scale_y_continuous(expand= c(0, 0)) +  # remove the space between columns and x axis
    labs(title = "", x = "Responses", y = "Percentage", fill = "Category") +  # set the names for main, x and y axises Subjective Responses for E205
    scale_fill_grey() +  # set the color for columns
    # geom_text(label = c("***", "***", "", ""), color = c("red", "red", "red", "red"), size = 6, nudge_y = 0.05, nudge_x = rep(c(-0.225, 0.225), 2)) + # add starts to the significant columns
    erp_theme
}

acc.ColuPlot.E205.Block

df.ratio.E205.Block <- {
  clean_beha_205 %>%
    filter(Type == "intact") %>% 
    mutate(isFace = if_else(Category == "face", 1, 0)) %>% 
    group_by(SubjCode, blockID, Duration, Resp) %>% 
    summarize(ratio = mean(isFace), Count = n())
}

desc.ratio.E205.Block <- {
  df.ratio.E205.Block %>%
    group_by(blockID, Duration, Resp) %>%
    summarize(Mean = mean(ratio)
              , N = n()
              , SD = if_else(N > 1, sd(ratio), 0)
              , SE = SD/sqrt(N)
              , SE.lo = Mean - SE, SE.hi = Mean + SE
              , CI = SE * qt(0.975,N)
              , Median = median(ratio)
              , Lower = Mean - SD, Upper = Mean + SD # for rainclound plot
    ) %>% 
    ungroup()
}

ratio.ColuPlot.E205.Block <- {
  ggplot(data = desc.ratio.E205.Block, aes(y = Mean, x = Resp)) +  # set the data, varialbes for x and y axises, and the variable for dividing data
    geom_col(position = "dodge", color = "black", alpha = .7) +  # position of columns and countour of columns
    facet_grid(Duration ~ blockID,
               labeller = labeller(Duration = dura.labels,
                                   blockID = bloc.labels)) +
    geom_errorbar(mapping = aes(ymin = SE.lo, ymax = SE.hi), linetype = 1,  # set the error bar
                  show.legend = FALSE, width = 0.25, alpha = .5,
                  position = position_dodge(width=0.9)) +
    geom_hline(yintercept = c(0.5, 1), linetype = 5, alpha = 0.5) +  # add the line for 0.5 and 1 (y)
    coord_cartesian(ylim = c(0,1.1)) +  # set the limit for y axis
    scale_y_continuous(expand= c(0, 0)) +  # remove the space between columns and x axis
    labs(title = "", x = "Responses", y = "Percentage") +  # set the names for main, x and y axises Subjective Responses for E205
    scale_fill_grey() +  # set the color for columns
    # geom_text(label = c("***", "***", "", ""), color = c("red", "red", "red", "red"), size = 6, nudge_y = 0.05, nudge_x = rep(c(-0.225, 0.225), 2)) + # add starts to the significant columns
    plot_theme
}

ratio.ColuPlot.E205.Block

df.erp.high.E2 <- {
  df.erp.SC.E2 %>% 
    filter(Confidence == "high") %>% 
    mutate(DuraBlock = paste(Duration, Block_ST_table, sep = "_")) %>% 
    sdif_coding_E205_erp_conf()
}

5.4.1 Amplitudes of the P1

# only keep the data for amplitudes of the P1 (correct and incorrect erps)
df.erp.P1.high.E2 <- {
  df.erp.high.E2 %>% 
    filter(Component == "P1")
}

if (saveCSV) {
  output.erp.P1.high.E2 <- file.path(folder_nesi, "E205_erp_P1_high.RData")
  save(df.erp.P1.high.E2, file = output.erp.P1.high.E2)
}

# the structure of this dataset
head(df.erp.P1.high.E2, 10)

## # A tibble: 10 x 83
##    Channel Event Urevent TrialNumber Block_ST_table Response urResponse    RT  urRT Reject Rejthresh Rejjp SubjCode ChanCent ChanCluster_1 ChanCluster_2 ChanCluster_3 ChanCluster_4 ChanCluster_5 ChanCluster_6 ChanCluster_7 Hemisphere Component MeanAmp MethodCode Method        IsPositive StartFrame PeakFrame EndFrame StartTime PeakTime EndTime FrameSize WindowSize StartAmplitude PeakAmplitude EndAmplitude Session Block_beha_table Trial Resp_ACC Resp_OnsetDelay Resp_RT stimName   Type   Category Duration Stimuli Confidence DuraConf Type_C Cate_C Dura_C Type_Cate Type_Dura Cate_Dura Type_Cate_Dura Hemi_C Type_Hemi Cate_Hemi Dura_Hemi Type_Cate_Hemi Type_Dura_Hemi Cate_Dura_Hemi Type_Cate_Dura_Hemi ConLG_C ConHL_C Con2H_C Cate_ConLH Hemi_ConLH Cate_Hemi_ConLH Cate_ConGL Hemi_ConGL Cate_Hemi_ConGL Cate_Con2G Hemi_Con2G Cate_Hemi_Con2G DuraBlock ConLH_C ConGL_C Block21_C Block22_C
##    <chr>   <chr>   <dbl>       <dbl>          <dbl>    <dbl> <fct>      <dbl> <dbl>  <dbl>     <dbl> <dbl> <fct>    <chr>    <chr>         <chr>         <chr>         <chr>         <chr>         <chr>         <chr>         <fct>      <chr>       <dbl>      <dbl> <chr>              <dbl>      <dbl>     <dbl>    <dbl>     <dbl>    <dbl>   <dbl>     <dbl>      <dbl>          <dbl>         <dbl>        <dbl>   <dbl>            <dbl> <dbl>    <dbl>           <dbl>   <dbl> <chr>      <fct>  <fct>    <fct>    <chr>   <fct>      <fct>     <dbl>  <dbl>  <dbl>     <dbl>     <dbl>     <dbl>          <dbl>  <dbl>     <dbl>     <dbl>     <dbl>          <dbl>          <dbl>          <dbl>               <dbl>   <dbl>   <dbl>   <dbl>      <dbl>      <dbl>           <dbl>      <dbl>      <dbl>           <dbl>      <dbl>      <dbl>           <dbl> <fct>       <dbl>   <dbl>     <dbl>     <dbl>
##  1 E65     NF2+     2590         646              2       11 RE11         706   756      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       P1         -1.08           2 MeanAmplitude          1         24        29       34        96      116     136        10         40         -5.91           2.99       -4.57        1               35    11        1              16     540 NfF040.bmp intact face     200      fF040   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.167     -0.167          0.0833     -0.333     -0.333           0.167      0.125      0.125         -0.0625 200_2       0.333   0.667     0.333     0.667
##  2 E65     NF2+     2610         651              2       11 RE11         564   614      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       P1          2.91           2 MeanAmplitude          1         24        34       34        96      136     136        10         40          1.44           7.07        7.07        1               35    16        1              17     397 NfF078.bmp intact face     200      fF078   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.167     -0.167          0.0833     -0.333     -0.333           0.167      0.125      0.125         -0.0625 200_2       0.333   0.667     0.333     0.667
##  3 E65     NF2+     2622         654              2       11 RE11         574   624      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       P1          2.35           2 MeanAmplitude          1         24        27       34        96      108     136        10         40         -1.17           5.15        3.13        1               35    19        1              16     408 NfM122.bmp intact face     200      fM122   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.167     -0.167          0.0833     -0.333     -0.333           0.167      0.125      0.125         -0.0625 200_2       0.333   0.667     0.333     0.667
##  4 E65     NF2+     2626         655              2       11 RE11         387   437      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       P1         10.7            2 MeanAmplitude          1         24        25       34        96      100     136        10         40         11.5           12.1         9.94        1               35    20        1              17     220 NfM013.bmp intact face     200      fM013   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.167     -0.167          0.0833     -0.333     -0.333           0.167      0.125      0.125         -0.0625 200_2       0.333   0.667     0.333     0.667
##  5 E65     NF2+     2666         665              2       11 RE11         361   411      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       P1          0.608          2 MeanAmplitude          1         24        28       34        96      112     136        10         40         -5.60           4.06        0.541       1               35    30        1              16     195 NfM142.bmp intact face     200      fM142   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.167     -0.167          0.0833     -0.333     -0.333           0.167      0.125      0.125         -0.0625 200_2       0.333   0.667     0.333     0.667
##  6 E65     NF2+     2686         670              2       11 RE11         366   416      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       P1         -1.54           2 MeanAmplitude          1         24        29       34        96      116     136        10         40         -6.76           1.08       -1.13        1               35    35        1              16     200 NfF112.bmp intact face     200      fF112   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.167     -0.167          0.0833     -0.333     -0.333           0.167      0.125      0.125         -0.0625 200_2       0.333   0.667     0.333     0.667
##  7 E65     NF2+     2690         671              2       11 RE11         423   473      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       P1          3.35           2 MeanAmplitude          1         24        30       34        96      120     136        10         40          1.09           7.85       -0.560       1               35    36        1              17     256 NfF017.bmp intact face     200      fF017   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.167     -0.167          0.0833     -0.333     -0.333           0.167      0.125      0.125         -0.0625 200_2       0.333   0.667     0.333     0.667
##  8 E65     NF2+     2722         679              2       11 RE11         446   496      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       P1          6.01           2 MeanAmplitude          1         24        34       34        96      136     136        10         40          0.630          9.21        9.21        1               35    44        1              17     279 NfF068.bmp intact face     200      fF068   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.167     -0.167          0.0833     -0.333     -0.333           0.167      0.125      0.125         -0.0625 200_2       0.333   0.667     0.333     0.667
##  9 E65     NF2+     2778         693              2       11 RE11         454   504      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       P1          2.87           2 MeanAmplitude          1         24        31       34        96      124     136        10         40         -2.85           6.41        1.56        1               35    58        1              16     288 NfF076.bmp intact face     200      fF076   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.167     -0.167          0.0833     -0.333     -0.333           0.167      0.125      0.125         -0.0625 200_2       0.333   0.667     0.333     0.667
## 10 E65     NF2+     2790         696              2       11 RE11         441   491      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       P1          0.793          2 MeanAmplitude          1         24        28       34        96      112     136        10         40          0.139          2.21        0.914       1               35    61        1              16     275 NfF079.bmp intact face     200      fF079   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.167     -0.167          0.0833     -0.333     -0.333           0.167      0.125      0.125         -0.0625 200_2       0.333   0.667     0.333     0.667

5.4.1.1 The maximal model

lmm.max.P1.high.E2 <- lmer(MeanAmp ~ Category * Hemisphere * DuraBlock +
                             (1 + Cate_C + Hemi_C + Cate_Hemi | SubjCode) +
                             (1 + Hemi_C | Stimuli),
                           data = df.erp.P1.high.E2,
                           verbose = TRUE,
                           control = lmerControl(optimizer = "bobyqa",  # nloptwrap Nelder_Mead
                                                 optCtrl = list(maxfun = 1e5)))

## start par. =  1 0 1 1 0 0 0 1 0 0 1 0 1 fn =  104824.6 
## At return
## eval: 865 fn:      103991.60 par: 0.0607829 0.0132275  0.00000 0.368838 -0.0559286 0.0565163 0.0161717 0.107212 0.0588553 -0.00506727 0.178642 -0.0172575  0.00000

print(summary(lmm.max.P1.high.E2), corr = FALSE)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category * Hemisphere * DuraBlock + (1 + Cate_C + Hemi_C +      Cate_Hemi | SubjCode) + (1 + Hemi_C | Stimuli)
##    Data: df.erp.P1.high.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
## 
## REML criterion at convergence: 103991.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.3462 -0.5802 -0.0117  0.5811  6.3206 
## 
## Random effects:
##  Groups   Name        Variance  Std.Dev. Corr             
##  Stimuli  (Intercept)  0.059710 0.24436                   
##           Hemi_C       0.002828 0.05318  1.00             
##  SubjCode (Intercept)  2.198631 1.48278                   
##           Cate_C       0.236320 0.48613  -0.46            
##           Hemi_C       0.623365 0.78953   0.29  0.13      
##           Cate_Hemi    0.009455 0.09724   0.67 -0.49 -0.52
##  Residual             16.161464 4.02013                   
## Number of obs: 18456, groups:  Stimuli, 80; SubjCode, 28
## 
## Fixed effects:
##                                          Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)                             1.379e+00  2.848e-01  2.794e+01   4.843 4.29e-05 ***
## Category2-1                            -3.119e-03  1.354e-01  4.647e+01  -0.023    0.982    
## Hemisphere2-1                           1.101e-01  1.695e-01  3.103e+01   0.650    0.521    
## DuraBlock2-1                           -3.110e-02  9.951e-02  1.778e+04  -0.313    0.755    
## DuraBlock3-2                            1.062e-01  9.877e-02  1.193e+04   1.075    0.282    
## Category2-1:Hemisphere2-1              -1.317e-01  1.491e-01  1.563e+03  -0.883    0.377    
## Category2-1:DuraBlock2-1               -2.797e-02  1.988e-01  1.724e+04  -0.141    0.888    
## Category2-1:DuraBlock3-2                1.071e-01  1.947e-01  5.630e+03   0.550    0.582    
## Hemisphere2-1:DuraBlock2-1              4.922e-02  1.975e-01  1.801e+04   0.249    0.803    
## Hemisphere2-1:DuraBlock3-2              1.643e-02  1.924e-01  1.163e+04   0.085    0.932    
## Category2-1:Hemisphere2-1:DuraBlock2-1 -1.170e-02  3.940e-01  1.818e+04  -0.030    0.976    
## Category2-1:Hemisphere2-1:DuraBlock3-2 -1.721e-01  3.737e-01  1.591e+04  -0.461    0.645    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular

5.4.1.2 The zero-correlation-parameter model

lmm.zcp.P1.high.E2 <- update(lmm.max.P1.high.E2,
                             formula = MeanAmp ~ Category * Hemisphere * DuraBlock +
                               (1 + Cate_C + Hemi_C + Cate_Hemi || SubjCode) +
                               (1 + Hemi_C || Stimuli))

## start par. =  1 1 1 1 1 1 fn =  104824.6 
## At return
## eval: 349 fn:      103999.59 par:  0.00000 0.0606616  0.00000 0.196283 0.126125 0.371879

print(summary(lmm.zcp.P1.high.E2), corr = FALSE)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category + Hemisphere + DuraBlock + (1 + Cate_C + Hemi_C +      Cate_Hemi || SubjCode) + (1 + Hemi_C || Stimuli) + Category:Hemisphere +      Category:DuraBlock + Hemisphere:DuraBlock + Category:Hemisphere:DuraBlock
##    Data: df.erp.P1.high.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
## 
## REML criterion at convergence: 103999.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.3295 -0.5808 -0.0128  0.5814  6.3179 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev.
##  Stimuli    Hemi_C       0.00000 0.0000  
##  Stimuli.1  (Intercept)  0.05947 0.2439  
##  SubjCode   Cate_Hemi    0.00000 0.0000  
##  SubjCode.1 Hemi_C       0.62267 0.7891  
##  SubjCode.2 Cate_C       0.25710 0.5070  
##  SubjCode.3 (Intercept)  2.23512 1.4950  
##  Residual               16.16202 4.0202  
## Number of obs: 18456, groups:  Stimuli, 80; SubjCode, 28
## 
## Fixed effects:
##                                          Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)                             1.382e+00  2.872e-01  2.798e+01   4.811 4.66e-05 ***
## Category2-1                             6.224e-03  1.392e-01  4.769e+01   0.045    0.965    
## Hemisphere2-1                           1.018e-01  1.696e-01  3.174e+01   0.601    0.552    
## DuraBlock2-1                           -3.233e-02  9.964e-02  1.821e+04  -0.324    0.746    
## DuraBlock3-2                            1.035e-01  9.925e-02  1.579e+04   1.043    0.297    
## Category2-1:Hemisphere2-1              -1.448e-01  1.482e-01  1.785e+04  -0.977    0.328    
## Category2-1:DuraBlock2-1               -2.736e-02  1.991e-01  1.794e+04  -0.137    0.891    
## Category2-1:DuraBlock3-2                9.423e-02  1.962e-01  9.184e+03   0.480    0.631    
## Hemisphere2-1:DuraBlock2-1              4.850e-02  1.976e-01  1.831e+04   0.245    0.806    
## Hemisphere2-1:DuraBlock3-2              2.796e-02  1.929e-01  1.636e+04   0.145    0.885    
## Category2-1:Hemisphere2-1:DuraBlock2-1 -6.056e-03  3.941e-01  1.832e+04  -0.015    0.988    
## Category2-1:Hemisphere2-1:DuraBlock3-2 -1.563e-01  3.742e-01  1.830e+04  -0.418    0.676    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular

5.4.1.3 The reduced model

summary(rePCA(lmm.zcp.P1.high.E2))

## $Stimuli
## Importance of components:
##                           [,1] [,2]
## Standard deviation     0.06066    0
## Proportion of Variance 1.00000    0
## Cumulative Proportion  1.00000    1
## 
## $SubjCode
## Importance of components:
##                          [,1]   [,2]    [,3] [,4]
## Standard deviation     0.3719 0.1963 0.12613    0
## Proportion of Variance 0.7176 0.1999 0.08254    0
## Cumulative Proportion  0.7176 0.9175 1.00000    1

lmm.rdc.P1.high.E2 <- update(lmm.zcp.P1.high.E2,
                             formula = MeanAmp ~ Category * Hemisphere * DuraBlock +
                               (1 + Cate_C + Hemi_C || SubjCode) +
                               (1 | Stimuli))

## start par. =  1 1 1 1 fn =  104459.8 
## At return
## eval: 150 fn:      103999.59 par: 0.0606616 0.196283 0.126125 0.371878

print(summary(lmm.rdc.P1.high.E2), corr = FALSE)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category + Hemisphere + DuraBlock + (1 + Cate_C + Hemi_C ||      SubjCode) + (1 | Stimuli) + Category:Hemisphere + Category:DuraBlock +      Hemisphere:DuraBlock + Category:Hemisphere:DuraBlock
##    Data: df.erp.P1.high.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
## 
## REML criterion at convergence: 103999.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.3295 -0.5808 -0.0128  0.5814  6.3179 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev.
##  Stimuli    (Intercept)  0.05947 0.2439  
##  SubjCode   Hemi_C       0.62267 0.7891  
##  SubjCode.1 Cate_C       0.25710 0.5070  
##  SubjCode.2 (Intercept)  2.23510 1.4950  
##  Residual               16.16202 4.0202  
## Number of obs: 18456, groups:  Stimuli, 80; SubjCode, 28
## 
## Fixed effects:
##                                          Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)                             1.382e+00  2.872e-01  2.798e+01   4.811 4.66e-05 ***
## Category2-1                             6.224e-03  1.392e-01  4.769e+01   0.045    0.965    
## Hemisphere2-1                           1.018e-01  1.696e-01  3.174e+01   0.601    0.552    
## DuraBlock2-1                           -3.233e-02  9.964e-02  1.821e+04  -0.324    0.746    
## DuraBlock3-2                            1.035e-01  9.925e-02  1.579e+04   1.043    0.297    
## Category2-1:Hemisphere2-1              -1.448e-01  1.482e-01  1.785e+04  -0.977    0.328    
## Category2-1:DuraBlock2-1               -2.736e-02  1.991e-01  1.794e+04  -0.137    0.891    
## Category2-1:DuraBlock3-2                9.423e-02  1.962e-01  9.184e+03   0.480    0.631    
## Hemisphere2-1:DuraBlock2-1              4.850e-02  1.976e-01  1.831e+04   0.245    0.806    
## Hemisphere2-1:DuraBlock3-2              2.796e-02  1.929e-01  1.636e+04   0.145    0.885    
## Category2-1:Hemisphere2-1:DuraBlock2-1 -6.056e-03  3.941e-01  1.832e+04  -0.015    0.988    
## Category2-1:Hemisphere2-1:DuraBlock3-2 -1.563e-01  3.742e-01  1.830e+04  -0.418    0.676    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

anova(lmm.rdc.P1.high.E2, lmm.zcp.P1.high.E2, refit = FALSE)

## Data: df.erp.P1.high.E2
## Models:
## lmm.rdc.P1.high.E2: MeanAmp ~ Category + Hemisphere + DuraBlock + (1 + Cate_C + Hemi_C || 
## lmm.rdc.P1.high.E2:     SubjCode) + (1 | Stimuli) + Category:Hemisphere + Category:DuraBlock + 
## lmm.rdc.P1.high.E2:     Hemisphere:DuraBlock + Category:Hemisphere:DuraBlock
## lmm.zcp.P1.high.E2: MeanAmp ~ Category + Hemisphere + DuraBlock + (1 + Cate_C + Hemi_C + 
## lmm.zcp.P1.high.E2:     Cate_Hemi || SubjCode) + (1 + Hemi_C || Stimuli) + Category:Hemisphere + 
## lmm.zcp.P1.high.E2:     Category:DuraBlock + Hemisphere:DuraBlock + Category:Hemisphere:DuraBlock
##                    npar    AIC    BIC logLik deviance Chisq Df Pr(>Chisq)
## lmm.rdc.P1.high.E2   17 104034 104167 -52000   104000                    
## lmm.zcp.P1.high.E2   19 104038 104186 -52000   104000     0  2          1

5.4.1.4 The extended model

lmm.etd.P1.high.E2 <- update(lmm.rdc.P1.high.E2,
                             formula = MeanAmp ~ Category * Hemisphere * DuraBlock +
                               (1 + Cate_C + Hemi_C | SubjCode) +
                               (1 | Stimuli))

## start par. =  1 1 0 0 1 0 1 fn =  104459.8 
## At return
## eval: 323 fn:      103992.90 par: 0.0606162 0.368822 -0.0560008 0.0540837 0.107121 0.0594280 0.180418

print(summary(lmm.etd.P1.high.E2), corr = FALSE)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category + Hemisphere + DuraBlock + (1 + Cate_C + Hemi_C |      SubjCode) + (1 | Stimuli) + Category:Hemisphere + Category:DuraBlock +      Hemisphere:DuraBlock + Category:Hemisphere:DuraBlock
##    Data: df.erp.P1.high.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
## 
## REML criterion at convergence: 103992.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.3330 -0.5802 -0.0119  0.5800  6.3237 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr       
##  Stimuli  (Intercept)  0.05939 0.2437              
##  SubjCode (Intercept)  2.19869 1.4828              
##           Cate_C       0.23616 0.4860   -0.46      
##           Hemi_C       0.63049 0.7940    0.27  0.14
##  Residual             16.16335 4.0204              
## Number of obs: 18456, groups:  Stimuli, 80; SubjCode, 28
## 
## Fixed effects:
##                                          Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)                             1.379e+00  2.848e-01  2.794e+01   4.842 4.29e-05 ***
## Category2-1                            -3.143e-03  1.353e-01  4.641e+01  -0.023    0.982    
## Hemisphere2-1                           1.061e-01  1.703e-01  3.162e+01   0.623    0.538    
## DuraBlock2-1                           -3.146e-02  9.952e-02  1.778e+04  -0.316    0.752    
## DuraBlock3-2                            1.067e-01  9.878e-02  1.192e+04   1.081    0.280    
## Category2-1:Hemisphere2-1              -1.444e-01  1.481e-01  1.772e+04  -0.975    0.330    
## Category2-1:DuraBlock2-1               -2.871e-02  1.988e-01  1.724e+04  -0.144    0.885    
## Category2-1:DuraBlock3-2                1.074e-01  1.947e-01  5.619e+03   0.552    0.581    
## Hemisphere2-1:DuraBlock2-1              4.903e-02  1.976e-01  1.830e+04   0.248    0.804    
## Hemisphere2-1:DuraBlock3-2              2.236e-02  1.928e-01  1.603e+04   0.116    0.908    
## Category2-1:Hemisphere2-1:DuraBlock2-1 -9.847e-03  3.941e-01  1.832e+04  -0.025    0.980    
## Category2-1:Hemisphere2-1:DuraBlock3-2 -1.557e-01  3.742e-01  1.829e+04  -0.416    0.677    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

anova(lmm.etd.P1.high.E2, lmm.rdc.P1.high.E2, refit = FALSE)

## Data: df.erp.P1.high.E2
## Models:
## lmm.rdc.P1.high.E2: MeanAmp ~ Category + Hemisphere + DuraBlock + (1 + Cate_C + Hemi_C || 
## lmm.rdc.P1.high.E2:     SubjCode) + (1 | Stimuli) + Category:Hemisphere + Category:DuraBlock + 
## lmm.rdc.P1.high.E2:     Hemisphere:DuraBlock + Category:Hemisphere:DuraBlock
## lmm.etd.P1.high.E2: MeanAmp ~ Category + Hemisphere + DuraBlock + (1 + Cate_C + Hemi_C | 
## lmm.etd.P1.high.E2:     SubjCode) + (1 | Stimuli) + Category:Hemisphere + Category:DuraBlock + 
## lmm.etd.P1.high.E2:     Hemisphere:DuraBlock + Category:Hemisphere:DuraBlock
##                    npar    AIC    BIC logLik deviance  Chisq Df Pr(>Chisq)  
## lmm.rdc.P1.high.E2   17 104034 104167 -52000   104000                       
## lmm.etd.P1.high.E2   20 104033 104189 -51996   103993 6.6975  3    0.08219 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.4.1.5 The optimal model

lmm.opt.P1.high.E205 <- lmm.etd.P1.high.E2

summary(lmm.opt.P1.high.E205)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category + Hemisphere + DuraBlock + (1 + Cate_C + Hemi_C |      SubjCode) + (1 | Stimuli) + Category:Hemisphere + Category:DuraBlock +      Hemisphere:DuraBlock + Category:Hemisphere:DuraBlock
##    Data: df.erp.P1.high.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
## 
## REML criterion at convergence: 103992.9
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.3330 -0.5802 -0.0119  0.5800  6.3237 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr       
##  Stimuli  (Intercept)  0.05939 0.2437              
##  SubjCode (Intercept)  2.19869 1.4828              
##           Cate_C       0.23616 0.4860   -0.46      
##           Hemi_C       0.63049 0.7940    0.27  0.14
##  Residual             16.16335 4.0204              
## Number of obs: 18456, groups:  Stimuli, 80; SubjCode, 28
## 
## Fixed effects:
##                                          Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)                             1.379e+00  2.848e-01  2.794e+01   4.842 4.29e-05 ***
## Category2-1                            -3.143e-03  1.353e-01  4.641e+01  -0.023    0.982    
## Hemisphere2-1                           1.061e-01  1.703e-01  3.162e+01   0.623    0.538    
## DuraBlock2-1                           -3.146e-02  9.952e-02  1.778e+04  -0.316    0.752    
## DuraBlock3-2                            1.067e-01  9.878e-02  1.192e+04   1.081    0.280    
## Category2-1:Hemisphere2-1              -1.444e-01  1.481e-01  1.772e+04  -0.975    0.330    
## Category2-1:DuraBlock2-1               -2.871e-02  1.988e-01  1.724e+04  -0.144    0.885    
## Category2-1:DuraBlock3-2                1.074e-01  1.947e-01  5.619e+03   0.552    0.581    
## Hemisphere2-1:DuraBlock2-1              4.903e-02  1.976e-01  1.830e+04   0.248    0.804    
## Hemisphere2-1:DuraBlock3-2              2.236e-02  1.928e-01  1.603e+04   0.116    0.908    
## Category2-1:Hemisphere2-1:DuraBlock2-1 -9.847e-03  3.941e-01  1.832e+04  -0.025    0.980    
## Category2-1:Hemisphere2-1:DuraBlock3-2 -1.557e-01  3.742e-01  1.829e+04  -0.416    0.677    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##               (Intr) Ctg2-1 Hms2-1 DrB2-1 DrB3-2 Ct2-1:H2-1 C2-1:DB2 C2-1:DB3 H2-1:DB2 H2-1:DB3 C2-1:H2-1:DB2
## Category2-1   -0.267                                                                                         
## Hemisphr2-1    0.238  0.089                                                                                  
## DuraBlck2-1    0.036  0.027 -0.001                                                                           
## DuraBlck3-2   -0.082 -0.193 -0.004 -0.660                                                                    
## Ctg2-1:H2-1    0.001  0.005  0.184 -0.001 -0.004                                                             
## Ct2-1:DB2-1    0.007  0.158 -0.001  0.442 -0.252 -0.001                                                      
## Ct2-1:DB3-2   -0.047 -0.321 -0.005 -0.254  0.333 -0.004     -0.674                                           
## Hm2-1:DB2-1    0.000 -0.001  0.131  0.001  0.001  0.070      0.001    0.001                                  
## Hm2-1:DB3-2   -0.001 -0.007 -0.242  0.001  0.004 -0.279      0.001    0.007   -0.685                         
## C2-1:H2-1:DB2  0.000  0.000  0.033  0.001  0.000  0.316      0.000    0.000    0.439   -0.268                
## C2-1:H2-1:DB3 -0.001 -0.002 -0.125  0.000  0.002 -0.468      0.000    0.002   -0.273    0.309   -0.714

anova(lmm.opt.P1.high.E205)

## Type III Analysis of Variance Table with Satterthwaite's method
##                                Sum Sq Mean Sq NumDF   DenDF F value Pr(>F)
## Category                       0.0087  0.0087     1    46.4  0.0005 0.9816
## Hemisphere                     6.2675  6.2675     1    31.6  0.3878 0.5379
## DuraBlock                     23.3871 11.6936     2  8969.0  0.7235 0.4851
## Category:Hemisphere           15.3536 15.3536     1 17716.0  0.9499 0.3298
## Category:DuraBlock             6.4551  3.2275     2  3445.7  0.1997 0.8190
## Hemisphere:DuraBlock           3.4887  1.7443     2 14233.6  0.1079 0.8977
## Category:Hemisphere:DuraBlock  6.2176  3.1088     2 18210.5  0.1923 0.8250

5.4.1.6 Diagnostic plots

qqplot_lmer(lmm.opt.P1.high.E205)

5.4.1.7 Estimated marginal means

emm.P1.high.E205 <- emmeans(lmm.opt.P1.high.E205, ~ DuraBlock + Category + Hemisphere)

emm.P1.high.E205 %>% 
  as_tibble() %>% 
  arrange(DuraBlock, Category, Hemisphere)

## # A tibble: 12 x 8
##    DuraBlock Category Hemisphere emmean    SE    df lower.CL upper.CL
##    <fct>     <fct>    <fct>       <dbl> <dbl> <dbl>    <dbl>    <dbl>
##  1 17_1      face     Left         1.32 0.310  30.6    0.687     1.95
##  2 17_1      face     Right        1.43 0.342  29.8    0.730     2.13
##  3 17_1      house    Left         1.34 0.297  47.3    0.746     1.94
##  4 17_1      house    Right        1.37 0.338  41.6    0.684     2.05
##  5 17_2      face     Left         1.28 0.326  37.5    0.615     1.94
##  6 17_2      face     Right        1.44 0.357  35.2    0.714     2.16
##  7 17_2      house    Left         1.28 0.323  66.2    0.631     1.92
##  8 17_2      house    Right        1.34 0.360  54.0    0.621     2.06
##  9 200_2     face     Left         1.28 0.312  31.3    0.643     1.91
## 10 200_2     face     Right        1.54 0.344  30.4    0.840     2.24
## 11 200_2     house    Left         1.46 0.267  31.7    0.919     2.01
## 12 200_2     house    Right        1.48 0.311  30.1    0.841     2.11

5.4.1.8 Plots

# emmip(emm.P1.high.E205, Category ~ DuraBlock | Hemisphere, CIs = TRUE)

durablock.labels = c("17ms\nhalf 1", "17ms\nhalf 2", "200ms\nhalf 2")

p1.high.LinePlot = {
  ggplot(data = data.frame(emm.P1.high.E205), aes(y = emmean, x = DuraBlock, color = Category, group = Category)) +  # set the data, varialbes for x and y axises, and the variable for dividing data
    geom_point(size = 2) +
    geom_line(aes(linetype = Category), size = .85) +  # position = "dodge", alpha = .7
    scale_linetype_manual(values=c("solid", "dashed")) +
    geom_errorbar(mapping = aes(ymin = lower.CL, ymax = upper.CL), linetype = 1,  # set the error bar
                  show.legend = FALSE, width = 0.25, alpha = .5) + # ,                   position = position_dodge(width=0.9)
    facet_grid(. ~ Hemisphere, # switch = "x",
               labeller = labeller(Hemisphere = hemiLabel)) +
    scale_y_reverse(limits = ylimit_p1) +  # set the limit for y axis
    scale_x_discrete(labels = durablock.labels) +
    # scale_y_continuous(expand= c(0, 0)) +  # remove the space between columns and x axis
    labs(x = "Duration and Halves", y = expression(paste("Amplitudes (", mu, "V)")), fill = "Category") +  # set the names for main, x and y axises title = "", 
    # scale_x_discrete(labels = c("left", "right")) +
    # geom_text(label = c("", "", "", "", "***", "", "", ""), color = "red", size = 6, nudge_y = 0.5, nudge_x = 0.5) + # add starts to the significant columns
    theme_bw() +
    erp_theme
}

# p1.high.LinePlot.slide <- {
#   p1.high.LinePlot + 
#     theme(legend.position="right",
#           axis.line = element_line(color="black", size = 3))
# }
# ggsave('P1_high.png', p1.high.LinePlot.slide, width = 7.5, height = 4)

p1.high.LinePlot

duration_color <- c(brewer.pal(9, 'Blues')[c(6, 8)], brewer.pal(12, "Paired")[6]) 
category_color <- brewer.pal(8, 'Dark2')[c(6,1)]

p1_high_LinePlot = {
  ggplot(data = data.frame(emm.P1.high.E205), aes(y = emmean, x = DuraBlock, group = Category)) +  # set the data, varialbes for x and y axises, and the variable for dividing data
    geom_point(aes(fill = DuraBlock), size=4, shape=21, stroke=0) +
    scale_fill_manual(values = duration_color, guide = FALSE) +
    geom_line(aes(linetype = Category, color = Category), size = .85) +  # position = "dodge", alpha = .7
    scale_color_manual(values=category_color) +
    scale_linetype_manual(values=c("solid", "dashed")) +
    geom_errorbar(mapping = aes(ymin = lower.CL, ymax = upper.CL), linetype = 1,  # set the error bar
                  show.legend = FALSE, width = 0.25, alpha = .5) + # ,                   position = position_dodge(width=0.9)
    facet_grid(. ~ Hemisphere, # switch = "x",
               labeller = labeller(Hemisphere = hemiLabel)) +
    scale_y_reverse(limits = ylimit_p1) +  # set the limit for y axis
    scale_x_discrete(labels = durablock.labels) +
    # scale_y_continuous(expand= c(0, 0)) +  # remove the space between columns and x axis
    labs(x = "Duration and Halves", y = expression(paste("P1 Amplitudes (", mu, "V)")), fill = "Category") +  # set the names for main, x and y axises title = "", 
    theme_bw() +
    erp_theme +
    theme(
      axis.title.x = element_text(margin = margin(t = 10, r = 0, b = 0, l = 0)),
      legend.margin = margin(t = -10),
      legend.title=element_text(size=12), 
      legend.text=element_text(size=12),
    )
}

ggsave('p1_high_pub.pdf', p1_high_LinePlot, width = 7, height = 4.5)

5.4.1.9 Contrast

5.4.1.9.1 Main results

Comparisons of P1 amplitudes for (intact) faces with different durations and high confidence (left hemisphere):

contra_high_P1 <- contrast(emmeans(lmm.opt.P1.high.E205, ~ DuraBlock | Hemisphere + Category), 
                               "pairwise", adjust = "none", infer = TRUE)

contra_high_P1[1:3]

##  contrast     Hemisphere Category    estimate        SE       df   lower.CL  upper.CL t.ratio p.value
##  17_1 - 17_2  Left       face      0.04407997 0.1482603 18312.03 -0.2465240 0.3346840   0.297  0.7662
##  17_1 - 200_2 Left       face      0.04115602 0.1227113 15818.16 -0.1993721 0.2816842   0.335  0.7373
##  17_2 - 200_2 Left       face     -0.00292394 0.1591442 17711.71 -0.3148623 0.3090144  -0.018  0.9853
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95

summary(contra_high_P1[1:3], delta = delta_null, level = .9)

##  contrast     Hemisphere Category    estimate        SE       df   lower.CL  upper.CL t.ratio p.value
##  17_1 - 17_2  Left       face      0.04407997 0.1482603 18312.03 -0.1997988 0.2879588  -3.075  0.0011
##  17_1 - 200_2 Left       face      0.04115602 0.1227113 15818.16 -0.1606980 0.2430100  -3.739  0.0001
##  17_2 - 200_2 Left       face     -0.00292394 0.1591442 17711.71 -0.2647066 0.2588587  -3.123  0.0009
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

summary(contra_high_P1[1:3], delta = delta_null, side = ">")

##  contrast     Hemisphere Category    estimate        SE       df   lower.CL upper.CL t.ratio p.value
##  17_1 - 17_2  Left       face      0.04407997 0.1482603 18312.03 -0.1997988      Inf   3.670  0.0001
##  17_1 - 200_2 Left       face      0.04115602 0.1227113 15818.16 -0.1606980      Inf   4.410  <.0001
##  17_2 - 200_2 Left       face     -0.00292394 0.1591442 17711.71 -0.2647066      Inf   3.123  0.0009
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Statistics are tests of noninferiority with a threshold of 0.5 
## P values are right-tailed

summary(contra_high_P1[1:3], delta = delta_null, side = "<")

##  contrast     Hemisphere Category    estimate        SE       df lower.CL  upper.CL t.ratio p.value
##  17_1 - 17_2  Left       face      0.04407997 0.1482603 18312.03     -Inf 0.2879588  -3.075  0.0011
##  17_1 - 200_2 Left       face      0.04115602 0.1227113 15818.16     -Inf 0.2430100  -3.739  0.0001
##  17_2 - 200_2 Left       face     -0.00292394 0.1591442 17711.71     -Inf 0.2588587  -3.160  0.0008
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Statistics are tests of nonsuperiority with a threshold of 0.5 
## P values are left-tailed

Comparisons of P1 amplitudes for (intact) faces with different durations and high confidence (right hemisphere):

contra_high_P1[4:6]

##  contrast     Hemisphere Category    estimate        SE       df   lower.CL  upper.CL t.ratio p.value
##  17_1 - 17_2  Right      face     -0.00987683 0.1481876 18277.67 -0.3003384 0.2805847  -0.067  0.9469
##  17_1 - 200_2 Right      face     -0.11299380 0.1223560 14516.06 -0.3528272 0.1268396  -0.923  0.3558
##  17_2 - 200_2 Right      face     -0.10311696 0.1589035 17271.69 -0.4145839 0.2083500  -0.649  0.5164
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95

summary(contra_high_P1[4:6], delta = delta_null, level = .9)

##  contrast     Hemisphere Category    estimate        SE       df   lower.CL   upper.CL t.ratio p.value
##  17_1 - 17_2  Right      face     -0.00987683 0.1481876 18277.67 -0.2536361 0.23388241  -3.307  0.0005
##  17_1 - 200_2 Right      face     -0.11299380 0.1223560 14516.06 -0.3142644 0.08827682  -3.163  0.0008
##  17_2 - 200_2 Right      face     -0.10311696 0.1589035 17271.69 -0.3645040 0.15827006  -2.498  0.0063
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

summary(contra_high_P1[4:6], delta = delta_null, side = ">")

##  contrast     Hemisphere Category    estimate        SE       df   lower.CL upper.CL t.ratio p.value
##  17_1 - 17_2  Right      face     -0.00987683 0.1481876 18277.67 -0.2536361      Inf   3.307  0.0005
##  17_1 - 200_2 Right      face     -0.11299380 0.1223560 14516.06 -0.3142644      Inf   3.163  0.0008
##  17_2 - 200_2 Right      face     -0.10311696 0.1589035 17271.69 -0.3645040      Inf   2.498  0.0063
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Statistics are tests of noninferiority with a threshold of 0.5 
## P values are right-tailed

summary(contra_high_P1[4:6], delta = delta_null, side = "<")

##  contrast     Hemisphere Category    estimate        SE       df lower.CL   upper.CL t.ratio p.value
##  17_1 - 17_2  Right      face     -0.00987683 0.1481876 18277.67     -Inf 0.23388241  -3.441  0.0003
##  17_1 - 200_2 Right      face     -0.11299380 0.1223560 14516.06     -Inf 0.08827682  -5.010  <.0001
##  17_2 - 200_2 Right      face     -0.10311696 0.1589035 17271.69     -Inf 0.15827006  -3.795  0.0001
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Statistics are tests of nonsuperiority with a threshold of 0.5 
## P values are left-tailed

Comparisons of face-specific P1 amplitude components for (intact) faces with different durations and high confidence (left hemisphere):

contra_high_P1_spec <- contrast(emmeans(lmm.opt.P1.high.E205, ~ Category + DuraBlock | Hemisphere), # 
                                interaction = "pairwise", adjust = "Bonferroni", infer = TRUE)
contra_high_P1_spec[1:3]

##  Category_pairwise DuraBlock_pairwise Hemisphere    estimate        SE       df   lower.CL  upper.CL t.ratio p.value
##  face - house      17_1 - 17_2        Left       -0.02378827 0.2798066 18069.83 -0.6937019 0.6461254  -0.085  1.0000
##  face - house      17_1 - 200_2       Left        0.16146629 0.2148371  3745.20 -0.3530805 0.6760131   0.752  1.0000
##  face - house      17_2 - 200_2       Left        0.18525456 0.2697033 10769.69 -0.4605105 0.8310196   0.687  1.0000
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 3 estimates 
## P value adjustment: bonferroni method for 3 tests

summary(contra_high_P1_spec[1:3], delta = delta_null, level = .9)

##  Category_pairwise DuraBlock_pairwise Hemisphere    estimate        SE       df   lower.CL  upper.CL t.ratio p.value
##  face - house      17_1 - 17_2        Left       -0.02378827 0.2798066 18069.83 -0.6192748 0.5716983  -1.702  0.1332
##  face - house      17_1 - 200_2       Left        0.16146629 0.2148371  3745.20 -0.2958855 0.6188181  -1.576  0.1727
##  face - house      17_2 - 200_2       Left        0.18525456 0.2697033 10769.69 -0.3887599 0.7592690  -1.167  0.3649
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Conf-level adjustment: bonferroni method for 3 estimates 
## P value adjustment: bonferroni method for 3 tests 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

summary(contra_high_P1_spec[1:3], delta = delta_null, side = ">")

##  Category_pairwise DuraBlock_pairwise Hemisphere    estimate        SE       df   lower.CL upper.CL t.ratio p.value
##  face - house      17_1 - 17_2        Left       -0.02378827 0.2798066 18069.83 -0.6192748      Inf   1.702  0.1332
##  face - house      17_1 - 200_2       Left        0.16146629 0.2148371  3745.20 -0.2958855      Inf   3.079  0.0031
##  face - house      17_2 - 200_2       Left        0.18525456 0.2697033 10769.69 -0.3887599      Inf   2.541  0.0166
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 3 estimates 
## P value adjustment: bonferroni method for 3 tests 
## Statistics are tests of noninferiority with a threshold of 0.5 
## P values are right-tailed

summary(contra_high_P1_spec[1:3], delta = delta_null, side = "<")

##  Category_pairwise DuraBlock_pairwise Hemisphere    estimate        SE       df lower.CL  upper.CL t.ratio p.value
##  face - house      17_1 - 17_2        Left       -0.02378827 0.2798066 18069.83     -Inf 0.5716983  -1.872  0.0918
##  face - house      17_1 - 200_2       Left        0.16146629 0.2148371  3745.20     -Inf 0.6188181  -1.576  0.1727
##  face - house      17_2 - 200_2       Left        0.18525456 0.2697033 10769.69     -Inf 0.7592690  -1.167  0.3649
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 3 estimates 
## P value adjustment: bonferroni method for 3 tests 
## Statistics are tests of nonsuperiority with a threshold of 0.5 
## P values are left-tailed

Comparisons of face-specific P1 amplitude components for (intact) faces with different durations and high confidence (right hemisphere):

contra_high_P1_spec[4:6]

##  Category_pairwise DuraBlock_pairwise Hemisphere    estimate        SE       df   lower.CL  upper.CL t.ratio p.value
##  face - house      17_1 - 17_2        Right      -0.03363520 0.2799462 18184.19 -0.7038828 0.6366124  -0.120  1.0000
##  face - house      17_1 - 200_2       Right      -0.00405177 0.2160355  4863.00 -0.5214153 0.5133118  -0.019  1.0000
##  face - house      17_2 - 200_2       Right       0.02958343 0.2703208 12065.72 -0.6176494 0.6768162   0.109  1.0000
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 3 estimates 
## P value adjustment: bonferroni method for 3 tests

summary(contra_high_P1_spec[4:6], delta = delta_null, level = .9)

##  Category_pairwise DuraBlock_pairwise Hemisphere    estimate        SE       df   lower.CL  upper.CL t.ratio p.value
##  face - house      17_1 - 17_2        Right      -0.03363520 0.2799462 18184.19 -0.6294187 0.5621483  -1.666  0.1436
##  face - house      17_1 - 200_2       Right      -0.00405177 0.2160355  4863.00 -0.4639157 0.4558122  -2.296  0.0326
##  face - house      17_2 - 200_2       Right       0.02958343 0.2703208 12065.72 -0.5457374 0.6049042  -1.740  0.1228
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Conf-level adjustment: bonferroni method for 3 estimates 
## P value adjustment: bonferroni method for 3 tests 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

summary(contra_high_P1_spec[4:6], delta = delta_null, side = ">")

##  Category_pairwise DuraBlock_pairwise Hemisphere    estimate        SE       df   lower.CL upper.CL t.ratio p.value
##  face - house      17_1 - 17_2        Right      -0.03363520 0.2799462 18184.19 -0.6294187      Inf   1.666  0.1436
##  face - house      17_1 - 200_2       Right      -0.00405177 0.2160355  4863.00 -0.4639157      Inf   2.296  0.0326
##  face - house      17_2 - 200_2       Right       0.02958343 0.2703208 12065.72 -0.5457374      Inf   1.959  0.0752
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 3 estimates 
## P value adjustment: bonferroni method for 3 tests 
## Statistics are tests of noninferiority with a threshold of 0.5 
## P values are right-tailed

summary(contra_high_P1_spec[4:6], delta = delta_null, side = "<")

##  Category_pairwise DuraBlock_pairwise Hemisphere    estimate        SE       df lower.CL  upper.CL t.ratio p.value
##  face - house      17_1 - 17_2        Right      -0.03363520 0.2799462 18184.19     -Inf 0.5621483  -1.906  0.0850
##  face - house      17_1 - 200_2       Right      -0.00405177 0.2160355  4863.00     -Inf 0.4558122  -2.333  0.0295
##  face - house      17_2 - 200_2       Right       0.02958343 0.2703208 12065.72     -Inf 0.6049042  -1.740  0.1228
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 3 estimates 
## P value adjustment: bonferroni method for 3 tests 
## Statistics are tests of nonsuperiority with a threshold of 0.5 
## P values are left-tailed

5.4.1.9.2 Other results

Comparisons of P1 amplitudes for different durations and high confidence (without multiple comparison corrections):

summary(contra_high_P1[1:12], adjust = "none")

##  contrast     Hemisphere Category    estimate        SE       df   lower.CL  upper.CL t.ratio p.value
##  17_1 - 17_2  Left       face      0.04407997 0.1482603 18312.03 -0.2465241 0.3346840   0.297  0.7662
##  17_1 - 200_2 Left       face      0.04115602 0.1227113 15818.16 -0.1993721 0.2816842   0.335  0.7373
##  17_2 - 200_2 Left       face     -0.00292394 0.1591442 17711.71 -0.3148623 0.3090144  -0.018  0.9853
##  17_1 - 17_2  Right      face     -0.00987683 0.1481876 18277.67 -0.3003384 0.2805847  -0.067  0.9469
##  17_1 - 200_2 Right      face     -0.11299380 0.1223560 14516.06 -0.3528272 0.1268396  -0.923  0.3558
##  17_2 - 200_2 Right      face     -0.10311696 0.1589035 17271.69 -0.4145839 0.2083500  -0.649  0.5164
##  17_1 - 17_2  Left       house     0.06786824 0.2376349 17803.84 -0.3979193 0.5336558   0.286  0.7752
##  17_1 - 200_2 Left       house    -0.12031027 0.1820353  2753.57 -0.4772498 0.2366293  -0.661  0.5087
##  17_2 - 200_2 Left       house    -0.18817851 0.2212903  8660.11 -0.6219602 0.2456032  -0.850  0.3951
##  17_1 - 17_2  Right      house     0.02375836 0.2378914 18112.68 -0.4425314 0.4900481   0.100  0.9204
##  17_1 - 200_2 Right      house    -0.10894203 0.1843462  4769.53 -0.4703456 0.2524616  -0.591  0.5546
##  17_2 - 200_2 Right      house    -0.13270039 0.2226042 11722.18 -0.5690416 0.3036408  -0.596  0.5511
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95

summary(contra_high_P1[1:12], adjust = "none", delta = delta_null, level = .9)

##  contrast     Hemisphere Category    estimate        SE       df   lower.CL  upper.CL t.ratio p.value
##  17_1 - 17_2  Left       face      0.04407997 0.1482603 18312.03 -0.1997988 0.2879588  -3.075  0.0011
##  17_1 - 200_2 Left       face      0.04115602 0.1227113 15818.16 -0.1606980 0.2430100  -3.739  0.0001
##  17_2 - 200_2 Left       face     -0.00292394 0.1591442 17711.71 -0.2647066 0.2588587  -3.123  0.0009
##  17_1 - 17_2  Right      face     -0.00987683 0.1481876 18277.67 -0.2536361 0.2338824  -3.307  0.0005
##  17_1 - 200_2 Right      face     -0.11299380 0.1223560 14516.06 -0.3142644 0.0882768  -3.163  0.0008
##  17_2 - 200_2 Right      face     -0.10311696 0.1589035 17271.69 -0.3645040 0.1582701  -2.498  0.0063
##  17_1 - 17_2  Left       house     0.06786824 0.2376349 17803.84 -0.3230267 0.4587632  -1.818  0.0345
##  17_1 - 200_2 Left       house    -0.12031027 0.1820353  2753.57 -0.4198325 0.1792119  -2.086  0.0185
##  17_2 - 200_2 Left       house    -0.18817851 0.2212903  8660.11 -0.5522076 0.1758506  -1.409  0.0794
##  17_1 - 17_2  Right      house     0.02375836 0.2378914 18112.68 -0.3675582 0.4150749  -2.002  0.0227
##  17_1 - 200_2 Right      house    -0.10894203 0.1843462  4769.53 -0.4122234 0.1943394  -2.121  0.0170
##  17_2 - 200_2 Right      house    -0.13270039 0.2226042 11722.18 -0.4988806 0.2334798  -1.650  0.0495
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

5.4.2 Amplitudes of the N170

# only keep the data for amplitudes of the N170 (correct and incorrect erps)
df.erp.N170.high.E2 <- {
  df.erp.high.E2 %>% 
    filter(Component == "N170")
}

if (saveCSV) {
  output.erp.N170.high.E2 <- file.path(folder_nesi, "E205_erp_N170_high.RData")
  save(df.erp.N170.high.E2, file = output.erp.N170.high.E2)
}

# the structure of this dataset
head(df.erp.N170.high.E2, 10)

## # A tibble: 10 x 83
##    Channel Event Urevent TrialNumber Block_ST_table Response urResponse    RT  urRT Reject Rejthresh Rejjp SubjCode ChanCent ChanCluster_1 ChanCluster_2 ChanCluster_3 ChanCluster_4 ChanCluster_5 ChanCluster_6 ChanCluster_7 Hemisphere Component MeanAmp MethodCode Method        IsPositive StartFrame PeakFrame EndFrame StartTime PeakTime EndTime FrameSize WindowSize StartAmplitude PeakAmplitude EndAmplitude Session Block_beha_table Trial Resp_ACC Resp_OnsetDelay Resp_RT stimName   Type   Category Duration Stimuli Confidence DuraConf Type_C Cate_C Dura_C Type_Cate Type_Dura Cate_Dura Type_Cate_Dura Hemi_C Type_Hemi Cate_Hemi Dura_Hemi Type_Cate_Hemi Type_Dura_Hemi Cate_Dura_Hemi Type_Cate_Dura_Hemi ConLG_C ConHL_C Con2H_C Cate_ConLH Hemi_ConLH Cate_Hemi_ConLH Cate_ConGL Hemi_ConGL Cate_Hemi_ConGL Cate_Con2G Hemi_Con2G Cate_Hemi_Con2G DuraBlock ConLH_C ConGL_C Block21_C Block22_C
##    <chr>   <chr>   <dbl>       <dbl>          <dbl>    <dbl> <fct>      <dbl> <dbl>  <dbl>     <dbl> <dbl> <fct>    <chr>    <chr>         <chr>         <chr>         <chr>         <chr>         <chr>         <chr>         <fct>      <chr>       <dbl>      <dbl> <chr>              <dbl>      <dbl>     <dbl>    <dbl>     <dbl>    <dbl>   <dbl>     <dbl>      <dbl>          <dbl>         <dbl>        <dbl>   <dbl>            <dbl> <dbl>    <dbl>           <dbl>   <dbl> <chr>      <fct>  <fct>    <fct>    <chr>   <fct>      <fct>     <dbl>  <dbl>  <dbl>     <dbl>     <dbl>     <dbl>          <dbl>  <dbl>     <dbl>     <dbl>     <dbl>          <dbl>          <dbl>          <dbl>               <dbl>   <dbl>   <dbl>   <dbl>      <dbl>      <dbl>           <dbl>      <dbl>      <dbl>           <dbl>      <dbl>      <dbl>           <dbl> <fct>       <dbl>   <dbl>     <dbl>     <dbl>
##  1 E65     NF2+     2590         646              2       11 RE11         706   756      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       N170        -5.69          2 MeanAmplitude          0         38        43       48       152      172     192        10         40         -3.43         -10.2       -7.23         1               35    11        1              16     540 NfF040.bmp intact face     200      fF040   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.167     -0.167          0.0833     -0.333     -0.333           0.167      0.125      0.125         -0.0625 200_2       0.333   0.667     0.333     0.667
##  2 E65     NF2+     2610         651              2       11 RE11         564   614      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       N170        -2.28          2 MeanAmplitude          0         38        44       48       152      176     192        10         40         -0.667         -6.59       0.0234       1               35    16        1              17     397 NfF078.bmp intact face     200      fF078   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.167     -0.167          0.0833     -0.333     -0.333           0.167      0.125      0.125         -0.0625 200_2       0.333   0.667     0.333     0.667
##  3 E65     NF2+     2622         654              2       11 RE11         574   624      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       N170         3.67          2 MeanAmplitude          0         38        40       48       152      160     192        10         40          8.34           2.09       3.39         1               35    19        1              16     408 NfM122.bmp intact face     200      fM122   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.167     -0.167          0.0833     -0.333     -0.333           0.167      0.125      0.125         -0.0625 200_2       0.333   0.667     0.333     0.667
##  4 E65     NF2+     2626         655              2       11 RE11         387   437      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       N170         4.51          2 MeanAmplitude          0         38        41       48       152      164     192        10         40          7.44           3.17       8.05         1               35    20        1              17     220 NfM013.bmp intact face     200      fM013   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.167     -0.167          0.0833     -0.333     -0.333           0.167      0.125      0.125         -0.0625 200_2       0.333   0.667     0.333     0.667
##  5 E65     NF2+     2666         665              2       11 RE11         361   411      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       N170        -4.07          2 MeanAmplitude          0         38        43       48       152      172     192        10         40          2.85         -10.2       -4.62         1               35    30        1              16     195 NfM142.bmp intact face     200      fM142   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.167     -0.167          0.0833     -0.333     -0.333           0.167      0.125      0.125         -0.0625 200_2       0.333   0.667     0.333     0.667
##  6 E65     NF2+     2686         670              2       11 RE11         366   416      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       N170       -10.6           2 MeanAmplitude          0         38        40       48       152      160     192        10         40         -9.64         -11.6       -9.44         1               35    35        1              16     200 NfF112.bmp intact face     200      fF112   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.167     -0.167          0.0833     -0.333     -0.333           0.167      0.125      0.125         -0.0625 200_2       0.333   0.667     0.333     0.667
##  7 E65     NF2+     2690         671              2       11 RE11         423   473      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       N170         1.77          2 MeanAmplitude          0         38        43       48       152      172     192        10         40          0.612         -2.34       2.23         1               35    36        1              17     256 NfF017.bmp intact face     200      fF017   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.167     -0.167          0.0833     -0.333     -0.333           0.167      0.125      0.125         -0.0625 200_2       0.333   0.667     0.333     0.667
##  8 E65     NF2+     2722         679              2       11 RE11         446   496      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       N170         3.95          2 MeanAmplitude          0         38        41       48       152      164     192        10         40          5.19           1.21       7.96         1               35    44        1              17     279 NfF068.bmp intact face     200      fF068   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.167     -0.167          0.0833     -0.333     -0.333           0.167      0.125      0.125         -0.0625 200_2       0.333   0.667     0.333     0.667
##  9 E65     NF2+     2778         693              2       11 RE11         454   504      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       N170        -5.30          2 MeanAmplitude          0         38        48       48       152      192     192        10         40         -1.62          -7.56      -7.56         1               35    58        1              16     288 NfF076.bmp intact face     200      fF076   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.167     -0.167          0.0833     -0.333     -0.333           0.167      0.125      0.125         -0.0625 200_2       0.333   0.667     0.333     0.667
## 10 E65     NF2+     2790         696              2       11 RE11         441   491      0         0     0 P504     E65      E58           E59           E64           E65           E66           E69           E70           Left       N170        -1.28          2 MeanAmplitude          0         38        46       48       152      184     192        10         40         -1.24          -2.75      -1.00         1               35    61        1              16     275 NfF079.bmp intact face     200      fF079   high       200_high   -0.5   -0.5    0.5      0.25     -0.25     -0.25          0.125   -0.5      0.25      0.25     -0.25         -0.125          0.125          0.125             -0.0625    0.25     0.5   -0.25     -0.167     -0.167          0.0833     -0.333     -0.333           0.167      0.125      0.125         -0.0625 200_2       0.333   0.667     0.333     0.667

5.4.2.1 The maximal model

lmm.max.N170.high.E2 <- lmer(MeanAmp ~ Category * Hemisphere * DuraBlock +
                               (1 + Cate_C + Hemi_C + Cate_Hemi | SubjCode) +
                               (1 + Hemi_C | Stimuli),
                             data = df.erp.N170.high.E2,
                             verbose = TRUE,
                             control = lmerControl(optimizer = "bobyqa",  # nloptwrap Nelder_Mead
                                                   optCtrl = list(maxfun = 1e5)))

## start par. =  1 0 1 1 0 0 0 1 0 0 1 0 1 fn =  105474.1 
## At return
## eval: 1238 fn:      104763.30 par: 0.0573010 0.0277339  0.00000 0.454977 -0.134782 -0.0159491 -0.0974498 0.235465 -0.0364216 -0.107640 0.347868 -0.233303 0.243926

print(summary(lmm.max.N170.high.E2), corr = FALSE)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category * Hemisphere * DuraBlock + (1 + Cate_C + Hemi_C +      Cate_Hemi | SubjCode) + (1 + Hemi_C | Stimuli)
##    Data: df.erp.N170.high.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
## 
## REML criterion at convergence: 104763.3
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.6334 -0.5935 -0.0012  0.5937  6.1298 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr             
##  Stimuli  (Intercept)  0.05502 0.2346                    
##           Hemi_C       0.01289 0.1135   1.00             
##  SubjCode (Intercept)  3.46891 1.8625                    
##           Cate_C       1.23353 1.1106   -0.50            
##           Hemi_C       2.05437 1.4333   -0.05 -0.07      
##           Cate_Hemi    2.26250 1.5042   -0.27 -0.12 -0.59
##  Residual             16.75767 4.0936                    
## Number of obs: 18456, groups:  Stimuli, 80; SubjCode, 28
## 
## Fixed effects:
##                                          Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)                            -2.319e+00  3.558e-01  2.775e+01  -6.517 4.80e-07 ***
## Category2-1                             1.625e+00  2.339e-01  3.373e+01   6.949 5.42e-08 ***
## Hemisphere2-1                          -3.835e-01  2.846e-01  2.969e+01  -1.348    0.188    
## DuraBlock2-1                           -8.716e-01  1.016e-01  1.831e+04  -8.581  < 2e-16 ***
## DuraBlock3-2                            1.019e+00  1.014e-01  1.710e+04  10.052  < 2e-16 ***
## Category2-1:Hemisphere2-1               4.113e-01  3.317e-01  3.519e+01   1.240    0.223    
## Category2-1:DuraBlock2-1               -1.442e-01  2.030e-01  1.827e+04  -0.710    0.478    
## Category2-1:DuraBlock3-2                1.909e+00  2.018e-01  1.457e+04   9.460  < 2e-16 ***
## Hemisphere2-1:DuraBlock2-1             -1.955e-01  2.024e-01  1.765e+04  -0.966    0.334    
## Hemisphere2-1:DuraBlock3-2              4.096e-02  1.997e-01  1.025e+04   0.205    0.837    
## Category2-1:Hemisphere2-1:DuraBlock2-1  4.030e-01  4.045e-01  1.739e+04   0.996    0.319    
## Category2-1:Hemisphere2-1:DuraBlock3-2  3.516e-03  3.962e-01  6.953e+03   0.009    0.993    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular

5.4.2.2 The zero-correlation-parameter model

lmm.zcp.N170.high.E2 <- update(lmm.max.N170.high.E2,
                               formula = MeanAmp ~ Category * Hemisphere * DuraBlock +
                                 (1 + Cate_C + Hemi_C + Cate_Hemi || SubjCode) +
                                 (1 + Hemi_C || Stimuli))

## start par. =  1 1 1 1 1 1 fn =  105474.1 
## At return
## eval: 474 fn:      104787.06 par: 8.11255e-08 0.0562735 0.374364 0.356541 0.272915 0.456968

print(summary(lmm.zcp.N170.high.E2), corr = FALSE)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category + Hemisphere + DuraBlock + (1 + Cate_C + Hemi_C +      Cate_Hemi || SubjCode) + (1 + Hemi_C || Stimuli) + Category:Hemisphere +      Category:DuraBlock + Hemisphere:DuraBlock + Category:Hemisphere:DuraBlock
##    Data: df.erp.N170.high.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
## 
## REML criterion at convergence: 104787.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.6562 -0.5916  0.0000  0.5928  6.1351 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev.
##  Stimuli    Hemi_C       0.00000 0.0000  
##  Stimuli.1  (Intercept)  0.05308 0.2304  
##  SubjCode   Cate_Hemi    2.34918 1.5327  
##  SubjCode.1 Hemi_C       2.13082 1.4597  
##  SubjCode.2 Cate_C       1.24849 1.1174  
##  SubjCode.3 (Intercept)  3.50025 1.8709  
##  Residual               16.76210 4.0942  
## Number of obs: 18456, groups:  Stimuli, 80; SubjCode, 28
## 
## Fixed effects:
##                                          Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)                            -2.316e+00  3.574e-01  2.775e+01  -6.480 5.30e-07 ***
## Category2-1                             1.639e+00  2.352e-01  3.368e+01   6.968 5.16e-08 ***
## Hemisphere2-1                          -3.760e-01  2.898e-01  2.981e+01  -1.297    0.205    
## DuraBlock2-1                           -8.721e-01  1.017e-01  1.836e+04  -8.579  < 2e-16 ***
## DuraBlock3-2                            1.015e+00  1.017e-01  1.820e+04   9.981  < 2e-16 ***
## Category2-1:Hemisphere2-1               4.367e-01  3.389e-01  3.544e+01   1.289    0.206    
## Category2-1:DuraBlock2-1               -1.426e-01  2.032e-01  1.835e+04  -0.702    0.483    
## Category2-1:DuraBlock3-2                1.888e+00  2.028e-01  1.705e+04   9.314  < 2e-16 ***
## Hemisphere2-1:DuraBlock2-1             -1.984e-01  2.029e-01  1.829e+04  -0.978    0.328    
## Hemisphere2-1:DuraBlock3-2              3.172e-02  2.019e-01  1.734e+04   0.157    0.875    
## Category2-1:Hemisphere2-1:DuraBlock2-1  4.145e-01  4.057e-01  1.823e+04   1.022    0.307    
## Category2-1:Hemisphere2-1:DuraBlock3-2 -4.034e-02  4.019e-01  1.438e+04  -0.100    0.920    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular

5.4.2.3 The reduced model

summary(rePCA(lmm.zcp.N170.high.E2))

## $Stimuli
## Importance of components:
##                           [,1] [,2]
## Standard deviation     0.05627    0
## Proportion of Variance 1.00000    0
## Cumulative Proportion  1.00000    1
## 
## $SubjCode
## Importance of components:
##                          [,1]   [,2]   [,3]   [,4]
## Standard deviation     0.4570 0.3744 0.3565 0.2729
## Proportion of Variance 0.3793 0.2545 0.2309 0.1353
## Cumulative Proportion  0.3793 0.6338 0.8647 1.0000

lmm.rdc.N170.high.E2 <- update(lmm.zcp.N170.high.E2,
                               formula = MeanAmp ~ Category * Hemisphere * DuraBlock +
                                 (1 + Cate_C + Hemi_C + Cate_Hemi || SubjCode) +
                                 (1 | Stimuli))

## start par. =  1 1 1 1 1 fn =  105207.8 
## At return
## eval: 259 fn:      104787.06 par: 0.0562734 0.374363 0.356541 0.272917 0.456967

print(summary(lmm.rdc.N170.high.E2), corr = FALSE)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category + Hemisphere + DuraBlock + (1 + Cate_C + Hemi_C +      Cate_Hemi || SubjCode) + (1 | Stimuli) + Category:Hemisphere +      Category:DuraBlock + Hemisphere:DuraBlock + Category:Hemisphere:DuraBlock
##    Data: df.erp.N170.high.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
## 
## REML criterion at convergence: 104787.1
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.6562 -0.5916  0.0000  0.5928  6.1351 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev.
##  Stimuli    (Intercept)  0.05308 0.2304  
##  SubjCode   Cate_Hemi    2.34917 1.5327  
##  SubjCode.1 Hemi_C       2.13082 1.4597  
##  SubjCode.2 Cate_C       1.24850 1.1174  
##  SubjCode.3 (Intercept)  3.50024 1.8709  
##  Residual               16.76210 4.0942  
## Number of obs: 18456, groups:  Stimuli, 80; SubjCode, 28
## 
## Fixed effects:
##                                          Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)                            -2.316e+00  3.574e-01  2.775e+01  -6.480 5.30e-07 ***
## Category2-1                             1.639e+00  2.352e-01  3.368e+01   6.968 5.16e-08 ***
## Hemisphere2-1                          -3.760e-01  2.898e-01  2.981e+01  -1.297    0.205    
## DuraBlock2-1                           -8.721e-01  1.017e-01  1.836e+04  -8.579  < 2e-16 ***
## DuraBlock3-2                            1.015e+00  1.017e-01  1.820e+04   9.981  < 2e-16 ***
## Category2-1:Hemisphere2-1               4.367e-01  3.389e-01  3.544e+01   1.289    0.206    
## Category2-1:DuraBlock2-1               -1.426e-01  2.032e-01  1.835e+04  -0.702    0.483    
## Category2-1:DuraBlock3-2                1.888e+00  2.028e-01  1.705e+04   9.314  < 2e-16 ***
## Hemisphere2-1:DuraBlock2-1             -1.984e-01  2.029e-01  1.829e+04  -0.978    0.328    
## Hemisphere2-1:DuraBlock3-2              3.172e-02  2.019e-01  1.734e+04   0.157    0.875    
## Category2-1:Hemisphere2-1:DuraBlock2-1  4.145e-01  4.057e-01  1.823e+04   1.022    0.307    
## Category2-1:Hemisphere2-1:DuraBlock3-2 -4.034e-02  4.019e-01  1.438e+04  -0.100    0.920    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

anova(lmm.rdc.N170.high.E2, lmm.zcp.N170.high.E2, refit = FALSE)

## Data: df.erp.N170.high.E2
## Models:
## lmm.rdc.N170.high.E2: MeanAmp ~ Category + Hemisphere + DuraBlock + (1 + Cate_C + Hemi_C + 
## lmm.rdc.N170.high.E2:     Cate_Hemi || SubjCode) + (1 | Stimuli) + Category:Hemisphere + 
## lmm.rdc.N170.high.E2:     Category:DuraBlock + Hemisphere:DuraBlock + Category:Hemisphere:DuraBlock
## lmm.zcp.N170.high.E2: MeanAmp ~ Category + Hemisphere + DuraBlock + (1 + Cate_C + Hemi_C + 
## lmm.zcp.N170.high.E2:     Cate_Hemi || SubjCode) + (1 + Hemi_C || Stimuli) + Category:Hemisphere + 
## lmm.zcp.N170.high.E2:     Category:DuraBlock + Hemisphere:DuraBlock + Category:Hemisphere:DuraBlock
##                      npar    AIC    BIC logLik deviance Chisq Df Pr(>Chisq)
## lmm.rdc.N170.high.E2   18 104823 104964 -52394   104787                    
## lmm.zcp.N170.high.E2   19 104825 104974 -52394   104787     0  1          1

5.4.2.4 The extended model

lmm.etd.N170.high.E2 <- update(lmm.rdc.N170.high.E2,
                               formula = MeanAmp ~ Category * Hemisphere * DuraBlock +
                                 (1 + Cate_C + Hemi_C + Cate_Hemi | SubjCode) +
                                 (1 | Stimuli))

## start par. =  1 1 0 0 0 1 0 0 1 0 1 fn =  105207.8 
## At return
## eval: 903 fn:      104765.64 par: 0.0562480 0.454957 -0.134602 -0.0160593 -0.0976551 0.235449 -0.0361790 -0.107527 0.347685 -0.233521 0.243953

print(summary(lmm.etd.N170.high.E2), corr = FALSE)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category + Hemisphere + DuraBlock + (1 + Cate_C + Hemi_C +      Cate_Hemi | SubjCode) + (1 | Stimuli) + Category:Hemisphere +      Category:DuraBlock + Hemisphere:DuraBlock + Category:Hemisphere:DuraBlock
##    Data: df.erp.N170.high.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
## 
## REML criterion at convergence: 104765.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.6367 -0.5923 -0.0010  0.5935  6.1423 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr             
##  Stimuli  (Intercept)  0.05303 0.2303                    
##  SubjCode (Intercept)  3.46964 1.8627                    
##           Cate_C       1.23296 1.1104   -0.50            
##           Hemi_C       2.05262 1.4327   -0.05 -0.07      
##           Cate_Hemi    2.26537 1.5051   -0.27 -0.12 -0.59
##  Residual             16.76265 4.0942                    
## Number of obs: 18456, groups:  Stimuli, 80; SubjCode, 28
## 
## Fixed effects:
##                                          Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)                            -2.319e+00  3.558e-01  2.774e+01  -6.517 4.82e-07 ***
## Category2-1                             1.626e+00  2.336e-01  3.361e+01   6.960 5.34e-08 ***
## Hemisphere2-1                          -3.885e-01  2.842e-01  2.957e+01  -1.367    0.182    
## DuraBlock2-1                           -8.726e-01  1.016e-01  1.832e+04  -8.590  < 2e-16 ***
## DuraBlock3-2                            1.019e+00  1.014e-01  1.712e+04  10.054  < 2e-16 ***
## Category2-1:Hemisphere2-1               4.041e-01  3.309e-01  3.480e+01   1.221    0.230    
## Category2-1:DuraBlock2-1               -1.453e-01  2.031e-01  1.828e+04  -0.716    0.474    
## Category2-1:DuraBlock3-2                1.908e+00  2.018e-01  1.459e+04   9.454  < 2e-16 ***
## Hemisphere2-1:DuraBlock2-1             -1.971e-01  2.024e-01  1.765e+04  -0.974    0.330    
## Hemisphere2-1:DuraBlock3-2              5.056e-02  1.997e-01  1.025e+04   0.253    0.800    
## Category2-1:Hemisphere2-1:DuraBlock2-1  3.992e-01  4.045e-01  1.739e+04   0.987    0.324    
## Category2-1:Hemisphere2-1:DuraBlock3-2  1.533e-02  3.962e-01  6.960e+03   0.039    0.969    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

anova(lmm.etd.N170.high.E2, lmm.rdc.N170.high.E2, refit = FALSE)

## Data: df.erp.N170.high.E2
## Models:
## lmm.rdc.N170.high.E2: MeanAmp ~ Category + Hemisphere + DuraBlock + (1 + Cate_C + Hemi_C + 
## lmm.rdc.N170.high.E2:     Cate_Hemi || SubjCode) + (1 | Stimuli) + Category:Hemisphere + 
## lmm.rdc.N170.high.E2:     Category:DuraBlock + Hemisphere:DuraBlock + Category:Hemisphere:DuraBlock
## lmm.etd.N170.high.E2: MeanAmp ~ Category + Hemisphere + DuraBlock + (1 + Cate_C + Hemi_C + 
## lmm.etd.N170.high.E2:     Cate_Hemi | SubjCode) + (1 | Stimuli) + Category:Hemisphere + 
## lmm.etd.N170.high.E2:     Category:DuraBlock + Hemisphere:DuraBlock + Category:Hemisphere:DuraBlock
##                      npar    AIC    BIC logLik deviance  Chisq Df Pr(>Chisq)   
## lmm.rdc.N170.high.E2   18 104823 104964 -52394   104787                        
## lmm.etd.N170.high.E2   24 104814 105001 -52383   104766 21.414  6   0.001546 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.4.2.5 The optimal model

lmm.opt.N170.high.E205 <- lmm.etd.N170.high.E2

summary(lmm.opt.N170.high.E205)

## Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
## Formula: MeanAmp ~ Category + Hemisphere + DuraBlock + (1 + Cate_C + Hemi_C +      Cate_Hemi | SubjCode) + (1 | Stimuli) + Category:Hemisphere +      Category:DuraBlock + Hemisphere:DuraBlock + Category:Hemisphere:DuraBlock
##    Data: df.erp.N170.high.E2
## Control: lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
## 
## REML criterion at convergence: 104765.6
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -6.6367 -0.5923 -0.0010  0.5935  6.1423 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev. Corr             
##  Stimuli  (Intercept)  0.05303 0.2303                    
##  SubjCode (Intercept)  3.46964 1.8627                    
##           Cate_C       1.23296 1.1104   -0.50            
##           Hemi_C       2.05262 1.4327   -0.05 -0.07      
##           Cate_Hemi    2.26537 1.5051   -0.27 -0.12 -0.59
##  Residual             16.76265 4.0942                    
## Number of obs: 18456, groups:  Stimuli, 80; SubjCode, 28
## 
## Fixed effects:
##                                          Estimate Std. Error         df t value Pr(>|t|)    
## (Intercept)                            -2.319e+00  3.558e-01  2.774e+01  -6.517 4.82e-07 ***
## Category2-1                             1.626e+00  2.336e-01  3.361e+01   6.960 5.34e-08 ***
## Hemisphere2-1                          -3.885e-01  2.842e-01  2.957e+01  -1.367    0.182    
## DuraBlock2-1                           -8.726e-01  1.016e-01  1.832e+04  -8.590  < 2e-16 ***
## DuraBlock3-2                            1.019e+00  1.014e-01  1.712e+04  10.054  < 2e-16 ***
## Category2-1:Hemisphere2-1               4.041e-01  3.309e-01  3.480e+01   1.221    0.230    
## Category2-1:DuraBlock2-1               -1.453e-01  2.031e-01  1.828e+04  -0.716    0.474    
## Category2-1:DuraBlock3-2                1.908e+00  2.018e-01  1.459e+04   9.454  < 2e-16 ***
## Hemisphere2-1:DuraBlock2-1             -1.971e-01  2.024e-01  1.765e+04  -0.974    0.330    
## Hemisphere2-1:DuraBlock3-2              5.056e-02  1.997e-01  1.025e+04   0.253    0.800    
## Category2-1:Hemisphere2-1:DuraBlock2-1  3.992e-01  4.045e-01  1.739e+04   0.987    0.324    
## Category2-1:Hemisphere2-1:DuraBlock3-2  1.533e-02  3.962e-01  6.960e+03   0.039    0.969    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##               (Intr) Ctg2-1 Hms2-1 DrB2-1 DrB3-2 Ct2-1:H2-1 C2-1:DB2 C2-1:DB3 H2-1:DB2 H2-1:DB3 C2-1:H2-1:DB2
## Category2-1   -0.418                                                                                         
## Hemisphr2-1   -0.044 -0.061                                                                                  
## DuraBlck2-1    0.029  0.013  0.001                                                                           
## DuraBlck3-2   -0.070 -0.126  0.004 -0.651                                                                    
## Ctg2-1:H2-1   -0.228 -0.101 -0.415  0.002  0.009                                                             
## Ct2-1:DB2-1    0.004  0.089  0.001  0.444 -0.245  0.002                                                      
## Ct2-1:DB3-2   -0.041 -0.207  0.005 -0.246  0.345  0.011     -0.656                                           
## Hm2-1:DB2-1    0.000  0.001  0.075 -0.002 -0.001  0.024     -0.002   -0.001                                  
## Hm2-1:DB3-2    0.002  0.006 -0.162 -0.001 -0.009 -0.154     -0.002   -0.010   -0.667                         
## C2-1:H2-1:DB2  0.000  0.002  0.014 -0.002 -0.002  0.131     -0.003   -0.002    0.440   -0.254                
## C2-1:H2-1:DB3  0.002  0.008 -0.090 -0.001 -0.010 -0.267     -0.002   -0.013   -0.255    0.325   -0.674

anova(lmm.opt.N170.high.E205)

## Type III Analysis of Variance Table with Satterthwaite's method
##                                Sum Sq Mean Sq NumDF   DenDF F value    Pr(>F)    
## Category                       812.08  812.08     1    33.6 48.4458 5.341e-08 ***
## Hemisphere                      31.33   31.33     1    29.6  1.8690    0.1819    
## DuraBlock                     1815.59  907.79     2 16184.1 54.1557 < 2.2e-16 ***
## Category:Hemisphere             25.00   25.00     1    34.8  1.4916    0.2302    
## Category:DuraBlock            2383.57 1191.78     2 12707.4 71.0975 < 2.2e-16 ***
## Hemisphere:DuraBlock            20.63   10.31     2  7208.0  0.6153    0.5405    
## Category:Hemisphere:DuraBlock   31.57   15.78     2  4377.8  0.9415    0.3901    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

5.4.2.6 Diagnostic plots

qqplot_lmer(lmm.opt.N170.high.E205)

5.4.2.7 Estimated marginal means

emm.N170.high.E205 <- emmeans(lmm.opt.N170.high.E205, ~ DuraBlock + Category + Hemisphere)

emm.N170.high.E205 %>% 
  as_tibble() %>% 
  arrange(DuraBlock, Category, Hemisphere)

## # A tibble: 12 x 8
##    DuraBlock Category Hemisphere emmean    SE    df lower.CL upper.CL
##    <fct>     <fct>    <fct>       <dbl> <dbl> <dbl>    <dbl>    <dbl>
##  1 17_1      face     Left       -2.45  0.454  28.8    -3.38  -1.52  
##  2 17_1      face     Right      -2.79  0.474  28.5    -3.76  -1.82  
##  3 17_1      house    Left       -1.43  0.406  38.5    -2.25  -0.609 
##  4 17_1      house    Right      -1.64  0.345  42.4    -2.33  -0.940 
##  5 17_2      face     Left       -3.05  0.466  31.9    -4.00  -2.10  
##  6 17_2      face     Right      -3.79  0.485  31.3    -4.78  -2.80  
##  7 17_2      house    Left       -2.38  0.423  45.8    -3.23  -1.52  
##  8 17_2      house    Right      -2.58  0.367  54.4    -3.32  -1.85  
##  9 200_2     face     Left       -3.01  0.455  29.0    -3.94  -2.08  
## 10 200_2     face     Right      -3.70  0.474  28.7    -4.67  -2.73  
## 11 200_2     house    Left       -0.432 0.376  28.8    -1.20   0.338 
## 12 200_2     house    Right      -0.578 0.314  29.7    -1.22   0.0635

5.4.2.8 Plots

# emmip(emm.N170.high.E205, Category ~ DuraBlock | Hemisphere, CIs = TRUE) +
#   scale_y_continuous(trans = "reverse")

n170.high.LinePlot = {
  ggplot(data = data.frame(emm.N170.high.E205), aes(y = emmean, x = DuraBlock, color = Category, group = Category)) +  # set the data, varialbes for x and y axises, and the variable for dividing data
    geom_point(size = 2) +
    geom_line(aes(linetype = Category), size = .85) +  # position = "dodge", alpha = .7
    scale_linetype_manual(values=c("solid", "dashed")) +
    geom_errorbar(mapping = aes(ymin = lower.CL, ymax = upper.CL), linetype = 1,  # set the error bar
                  show.legend = FALSE, width = 0.25, alpha = .5) + # ,                   position = position_dodge(width=0.9)
    facet_grid(. ~ Hemisphere, # switch = "x",
               labeller = labeller(Hemisphere = hemiLabel)) +
    scale_y_reverse(limits = ylimit_n170, breaks = seq(1, -5, -1)) +  # set the limit for y axis
    scale_x_discrete(labels = durablock.labels) +
    # scale_y_continuous(expand= c(0, 0)) +  # remove the space between columns and x axis
    labs(x = "Duration and Halves", y = expression(paste("Amplitudes (", mu, "V)")), fill = "Category") +  # set the names for main, x and y axises title = "", 
    # scale_x_discrete(labels = c("left", "right")) +
    # geom_text(label = c("", "", "", "", "***", "", "", ""), color = "red", size = 6, nudge_y = 0.5, nudge_x = 0.5) + # add starts to the significant columns
    theme_bw() +
    erp_theme
}

# n170.high.LinePlot.slide <- {
#   n170.high.LinePlot + 
#     scale_y_reverse(limits = c(0.8, -4.8), breaks = seq(0, -4, -1)) +  # set the limit for y axis
#     theme(legend.position="right",
#           axis.line = element_line(color="black", size = 3))
# }
# ggsave('N170_high.png', n170.high.LinePlot.slide, width = 7.5, height = 4)

n170.high.LinePlot

duration_color <- c(brewer.pal(9, 'Blues')[c(6, 8)], brewer.pal(12, "Paired")[6]) 
category_color <- brewer.pal(8, 'Dark2')[c(6,1)]

n170_high_LinePlot = {
  ggplot(data = data.frame(emm.N170.high.E205), aes(y = emmean, x = DuraBlock, group = Category)) +  # set the data, varialbes for x and y axises, and the variable for dividing data
    geom_point(aes(fill = DuraBlock), size=4, shape=21, stroke=0) +
    scale_fill_manual(values = duration_color, guide = FALSE) +
    geom_line(aes(linetype = Category, color = Category), size = .85) +  # position = "dodge", alpha = .7
    scale_color_manual(values=category_color) +
    scale_linetype_manual(values=c("solid", "dashed")) +
    geom_errorbar(mapping = aes(ymin = lower.CL, ymax = upper.CL), linetype = 1,  # set the error bar
                  show.legend = FALSE, width = 0.25, alpha = .5) + # ,                   position = position_dodge(width=0.9)
    facet_grid(. ~ Hemisphere, # switch = "x",
               labeller = labeller(Hemisphere = hemiLabel)) +
    scale_y_reverse(limits = ylimit_n170, breaks = seq(1, -5, -1)) +  # set the limit for y axis
    scale_x_discrete(labels = durablock.labels) +
    # scale_y_continuous(expand= c(0, 0)) +  # remove the space between columns and x axis
    labs(x = "Duration and Halves", y = expression(paste("N170 Amplitudes (", mu, "V)")), fill = "Category") +  # set the names for main, x and y axises title = "", 
    theme_bw() +
    erp_theme +
    theme(
      axis.title.x = element_text(margin = margin(t = 10, r = 0, b = 0, l = 0)),
      legend.margin = margin(t = -10),
      legend.title=element_text(size=12), 
      legend.text=element_text(size=12),
    )
}

ggsave('n170_high_pub.pdf', n170_high_LinePlot, width = 7, height = 4.5)

5.4.2.9 Contrast

5.4.2.9.1 Main results

Comparisons of N170 amplitudes for (intact) faces with different durations and high confidence (left hemisphere):

contra_high_N170 <- contrast(emmeans(lmm.opt.N170.high.E205, ~ DuraBlock | Hemisphere + Category), # 
                                 "pairwise", adjust = "Bonferroni", infer = TRUE)

# left hemisphere
contra_high_N170[1:3]

##  contrast     Hemisphere Category   estimate        SE       df   lower.CL  upper.CL t.ratio p.value
##  17_1 - 17_2  Left       face      0.6015598 0.1514381 18306.67  0.2389867 0.9641330   3.972  0.0002
##  17_1 - 200_2 Left       face      0.5576724 0.1272494 17686.68  0.2530109 0.8623339   4.383  <.0001
##  17_2 - 200_2 Left       face     -0.0438874 0.1636515 18181.18 -0.4357021 0.3479273  -0.268  1.0000
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 3 estimates 
## P value adjustment: bonferroni method for 3 tests

summary(contra_high_N170[1:3], delta = delta_null, level = .9)

##  contrast     Hemisphere Category   estimate        SE       df   lower.CL  upper.CL t.ratio p.value
##  17_1 - 17_2  Left       face      0.6015598 0.1514381 18306.67  0.2792683 0.9238514   0.671  1.0000
##  17_1 - 200_2 Left       face      0.5576724 0.1272494 17686.68  0.2868588 0.8284861   0.453  1.0000
##  17_2 - 200_2 Left       face     -0.0438874 0.1636515 18181.18 -0.3921718 0.3043969  -2.787  0.0080
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Conf-level adjustment: bonferroni method for 3 estimates 
## P value adjustment: bonferroni method for 3 tests 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

summary(contra_high_N170[1:3], delta = delta_null, side = ">")

##  contrast     Hemisphere Category   estimate        SE       df   lower.CL upper.CL t.ratio p.value
##  17_1 - 17_2  Left       face      0.6015598 0.1514381 18306.67  0.2792683      Inf   7.274  <.0001
##  17_1 - 200_2 Left       face      0.5576724 0.1272494 17686.68  0.2868588      Inf   8.312  <.0001
##  17_2 - 200_2 Left       face     -0.0438874 0.1636515 18181.18 -0.3921718      Inf   2.787  0.0080
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 3 estimates 
## P value adjustment: bonferroni method for 3 tests 
## Statistics are tests of noninferiority with a threshold of 0.5 
## P values are right-tailed

summary(contra_high_N170[1:3], delta = delta_null, side = "<")

##  contrast     Hemisphere Category   estimate        SE       df lower.CL  upper.CL t.ratio p.value
##  17_1 - 17_2  Left       face      0.6015598 0.1514381 18306.67     -Inf 0.9238514   0.671  1.0000
##  17_1 - 200_2 Left       face      0.5576724 0.1272494 17686.68     -Inf 0.8284861   0.453  1.0000
##  17_2 - 200_2 Left       face     -0.0438874 0.1636515 18181.18     -Inf 0.3043969  -3.323  0.0013
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 3 estimates 
## P value adjustment: bonferroni method for 3 tests 
## Statistics are tests of nonsuperiority with a threshold of 0.5 
## P values are left-tailed

Comparisons of N170 amplitudes for (intact) faces with different durations and high confidence (right hemisphere):

# right hemisphere
contra_high_N170[4:6]

##  contrast     Hemisphere Category   estimate        SE       df   lower.CL  upper.CL t.ratio p.value
##  17_1 - 17_2  Right      face      0.9982659 0.1514036 18303.96  0.6357754 1.3607564   6.593  <.0001
##  17_1 - 200_2 Right      face      0.9114827 0.1270620 17334.05  0.6072693 1.2156961   7.174  <.0001
##  17_2 - 200_2 Right      face     -0.0867832 0.1635189 18079.17 -0.4782807 0.3047143  -0.531  1.0000
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 3 estimates 
## P value adjustment: bonferroni method for 3 tests

summary(contra_high_N170[4:6], delta = delta_null, level = .9)

##  contrast     Hemisphere Category   estimate        SE       df   lower.CL  upper.CL t.ratio p.value
##  17_1 - 17_2  Right      face      0.9982659 0.1514036 18303.96  0.6760478 1.3204839   3.291  1.0000
##  17_1 - 200_2 Right      face      0.9114827 0.1270620 17334.05  0.6410674 1.1818979   3.238  1.0000
##  17_2 - 200_2 Right      face     -0.0867832 0.1635189 18079.17 -0.4347855 0.2612191  -2.527  0.0173
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Conf-level adjustment: bonferroni method for 3 estimates 
## P value adjustment: bonferroni method for 3 tests 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

summary(contra_high_N170[4:6], delta = delta_null, side = ">")

##  contrast     Hemisphere Category   estimate        SE       df   lower.CL upper.CL t.ratio p.value
##  17_1 - 17_2  Right      face      0.9982659 0.1514036 18303.96  0.6760478      Inf   9.896  <.0001
##  17_1 - 200_2 Right      face      0.9114827 0.1270620 17334.05  0.6410674      Inf  11.109  <.0001
##  17_2 - 200_2 Right      face     -0.0867832 0.1635189 18079.17 -0.4347855      Inf   2.527  0.0173
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 3 estimates 
## P value adjustment: bonferroni method for 3 tests 
## Statistics are tests of noninferiority with a threshold of 0.5 
## P values are right-tailed

summary(contra_high_N170[4:6], delta = delta_null, side = "<")

##  contrast     Hemisphere Category   estimate        SE       df lower.CL  upper.CL t.ratio p.value
##  17_1 - 17_2  Right      face      0.9982659 0.1514036 18303.96     -Inf 1.3204839   3.291  1.0000
##  17_1 - 200_2 Right      face      0.9114827 0.1270620 17334.05     -Inf 1.1818979   3.238  1.0000
##  17_2 - 200_2 Right      face     -0.0867832 0.1635189 18079.17     -Inf 0.2612191  -3.588  0.0005
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 3 estimates 
## P value adjustment: bonferroni method for 3 tests 
## Statistics are tests of nonsuperiority with a threshold of 0.5 
## P values are left-tailed

Comparisons of N170 amplitude lateralization for (intact) faces with different durations and high confidence:

contra_high_N170_hemi <- contrast(emmeans(lmm.opt.N170.high.E205, ~ DuraBlock + Hemisphere | Category), # 
                                      interaction = "pairwise", adjust = "Bonferroni", infer = TRUE)
contra_high_N170_hemi[1:3]

##  DuraBlock_pairwise Hemisphere_pairwise Category   estimate        SE       df   lower.CL  upper.CL t.ratio p.value
##  17_1 - 17_2        Left - Right        face     -0.3967060 0.2140373 18283.98 -0.9091541 0.1157421  -1.853  0.1915
##  17_1 - 200_2       Left - Right        face     -0.3538102 0.1794494 16921.73 -0.7834513 0.0758308  -1.972  0.1460
##  17_2 - 200_2       Left - Right        face      0.0428958 0.2310594 17949.51 -0.5103077 0.5960992   0.186  1.0000
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 3 estimates 
## P value adjustment: bonferroni method for 3 tests

summary(contra_high_N170_hemi[1:3], delta = delta_null, level = .9)

##  DuraBlock_pairwise Hemisphere_pairwise Category   estimate        SE       df   lower.CL  upper.CL t.ratio p.value
##  17_1 - 17_2        Left - Right        face     -0.3967060 0.2140373 18283.98 -0.8522215 0.0588094  -0.483  0.9441
##  17_1 - 200_2       Left - Right        face     -0.3538102 0.1794494 16921.73 -0.7357179 0.0280975  -0.815  0.6229
##  17_2 - 200_2       Left - Right        face      0.0428958 0.2310594 17949.51 -0.4488470 0.5346386  -1.978  0.0719
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Conf-level adjustment: bonferroni method for 3 estimates 
## P value adjustment: bonferroni method for 3 tests 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

summary(contra_high_N170_hemi[1:3], delta = delta_null, side = ">")

##  DuraBlock_pairwise Hemisphere_pairwise Category   estimate        SE       df   lower.CL upper.CL t.ratio p.value
##  17_1 - 17_2        Left - Right        face     -0.3967060 0.2140373 18283.98 -0.8522215      Inf   0.483  0.9441
##  17_1 - 200_2       Left - Right        face     -0.3538102 0.1794494 16921.73 -0.7357179      Inf   0.815  0.6229
##  17_2 - 200_2       Left - Right        face      0.0428958 0.2310594 17949.51 -0.4488470      Inf   2.350  0.0282
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 3 estimates 
## P value adjustment: bonferroni method for 3 tests 
## Statistics are tests of noninferiority with a threshold of 0.5 
## P values are right-tailed

summary(contra_high_N170_hemi[1:3], delta = delta_null, side = "<")

##  DuraBlock_pairwise Hemisphere_pairwise Category   estimate        SE       df lower.CL  upper.CL t.ratio p.value
##  17_1 - 17_2        Left - Right        face     -0.3967060 0.2140373 18283.98     -Inf 0.0588094  -4.189  <.0001
##  17_1 - 200_2       Left - Right        face     -0.3538102 0.1794494 16921.73     -Inf 0.0280975  -4.758  <.0001
##  17_2 - 200_2       Left - Right        face      0.0428958 0.2310594 17949.51     -Inf 0.5346386  -1.978  0.0719
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 3 estimates 
## P value adjustment: bonferroni method for 3 tests 
## Statistics are tests of nonsuperiority with a threshold of 0.5 
## P values are left-tailed

Comparisons of face-specific N170 amplitude components for (intact) faces with different durations and high confidence (left hemisphere):

contra_high_N170_spec <- contrast(emmeans(lmm.opt.N170.high.E205, ~ Category + DuraBlock | Hemisphere), # 
                                  interaction = "pairwise", adjust = "Bonferroni", infer = TRUE)

contra_high_N170_spec[1:3]

##  Category_pairwise DuraBlock_pairwise Hemisphere   estimate        SE       df   lower.CL  upper.CL t.ratio p.value
##  face - house      17_1 - 17_2        Left       -0.3448969 0.2869752 18209.03 -1.0319733 0.3421795  -1.202  0.6883
##  face - house      17_1 - 200_2       Left        1.5555782 0.2363195  7112.00  0.9897001 2.1214563   6.583  <.0001
##  face - house      17_2 - 200_2       Left        1.9004751 0.2846656 13731.61  1.2189079 2.5820424   6.676  <.0001
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 3 estimates 
## P value adjustment: bonferroni method for 3 tests

summary(contra_high_N170_spec[1:3], delta = delta_null, level = .9)

##  Category_pairwise DuraBlock_pairwise Hemisphere   estimate        SE       df   lower.CL  upper.CL t.ratio p.value
##  face - house      17_1 - 17_2        Left       -0.3448969 0.2869752 18209.03 -0.9556395 0.2658457  -0.540  0.8833
##  face - house      17_1 - 200_2       Left        1.5555782 0.2363195  7112.00  1.0525818 2.0585746   4.467  1.0000
##  face - house      17_2 - 200_2       Left        1.9004751 0.2846656 13731.61  1.2946329 2.5063174   4.920  1.0000
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Conf-level adjustment: bonferroni method for 3 estimates 
## P value adjustment: bonferroni method for 3 tests 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

summary(contra_high_N170_spec[1:3], delta = delta_null, side = ">")

##  Category_pairwise DuraBlock_pairwise Hemisphere   estimate        SE       df   lower.CL upper.CL t.ratio p.value
##  face - house      17_1 - 17_2        Left       -0.3448969 0.2869752 18209.03 -0.9556395      Inf   0.540  0.8833
##  face - house      17_1 - 200_2       Left        1.5555782 0.2363195  7112.00  1.0525818      Inf   8.698  <.0001
##  face - house      17_2 - 200_2       Left        1.9004751 0.2846656 13731.61  1.2946329      Inf   8.433  <.0001
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 3 estimates 
## P value adjustment: bonferroni method for 3 tests 
## Statistics are tests of noninferiority with a threshold of 0.5 
## P values are right-tailed

summary(contra_high_N170_spec[1:3], delta = delta_null, side = "<")

##  Category_pairwise DuraBlock_pairwise Hemisphere   estimate        SE       df lower.CL  upper.CL t.ratio p.value
##  face - house      17_1 - 17_2        Left       -0.3448969 0.2869752 18209.03     -Inf 0.2658457  -2.944  0.0049
##  face - house      17_1 - 200_2       Left        1.5555782 0.2363195  7112.00     -Inf 2.0585746   4.467  1.0000
##  face - house      17_2 - 200_2       Left        1.9004751 0.2846656 13731.61     -Inf 2.5063174   4.920  1.0000
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 3 estimates 
## P value adjustment: bonferroni method for 3 tests 
## Statistics are tests of nonsuperiority with a threshold of 0.5 
## P values are left-tailed

Comparisons of face-specific N170 amplitude components for (intact) faces with different durations and high confidence (right hemisphere):

# right
contra_high_N170_spec[4:6]

##  Category_pairwise DuraBlock_pairwise Hemisphere  estimate        SE       df   lower.CL  upper.CL t.ratio p.value
##  face - house      17_1 - 17_2        Right      0.0542572 0.2862422 17571.01 -0.6310664 0.7395809   0.190  1.0000
##  face - house      17_1 - 200_2       Right      1.9700598 0.2297485  2326.23  1.4196483 2.5204714   8.575  <.0001
##  face - house      17_2 - 200_2       Right      1.9158026 0.2809292  7615.60  1.2431151 2.5884901   6.820  <.0001
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 3 estimates 
## P value adjustment: bonferroni method for 3 tests

summary(contra_high_N170_spec[4:6], delta = delta_null, level = .9)

##  Category_pairwise DuraBlock_pairwise Hemisphere  estimate        SE       df  lower.CL  upper.CL t.ratio p.value
##  face - house      17_1 - 17_2        Right      0.0542572 0.2862422 17571.01 -0.554927 0.6634415  -1.557  0.1792
##  face - house      17_1 - 200_2       Right      1.9700598 0.2297485  2326.23  1.480854 2.4592658   6.399  1.0000
##  face - house      17_2 - 200_2       Right      1.9158026 0.2809292  7615.60  1.317864 2.5137412   5.040  1.0000
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Conf-level adjustment: bonferroni method for 3 estimates 
## P value adjustment: bonferroni method for 3 tests 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

summary(contra_high_N170_spec[4:6], delta = delta_null, side = ">")

##  Category_pairwise DuraBlock_pairwise Hemisphere  estimate        SE       df  lower.CL upper.CL t.ratio p.value
##  face - house      17_1 - 17_2        Right      0.0542572 0.2862422 17571.01 -0.554927      Inf   1.936  0.0793
##  face - house      17_1 - 200_2       Right      1.9700598 0.2297485  2326.23  1.480854      Inf  10.751  <.0001
##  face - house      17_2 - 200_2       Right      1.9158026 0.2809292  7615.60  1.317864      Inf   8.599  <.0001
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 3 estimates 
## P value adjustment: bonferroni method for 3 tests 
## Statistics are tests of noninferiority with a threshold of 0.5 
## P values are right-tailed

summary(contra_high_N170_spec[4:6], delta = delta_null, side = "<")

##  Category_pairwise DuraBlock_pairwise Hemisphere  estimate        SE       df lower.CL  upper.CL t.ratio p.value
##  face - house      17_1 - 17_2        Right      0.0542572 0.2862422 17571.01     -Inf 0.6634415  -1.557  0.1792
##  face - house      17_1 - 200_2       Right      1.9700598 0.2297485  2326.23     -Inf 2.4592658   6.399  1.0000
##  face - house      17_2 - 200_2       Right      1.9158026 0.2809292  7615.60     -Inf 2.5137412   5.040  1.0000
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 3 estimates 
## P value adjustment: bonferroni method for 3 tests 
## Statistics are tests of nonsuperiority with a threshold of 0.5 
## P values are left-tailed

Comparisons of face-specific N170 amplitude component lateralization for (intact) faces with different durations and high confidence:

contra_high_N170_spec_hemi <- contrast(emmeans(lmm.opt.N170.high.E205, ~ Category + DuraBlock + Hemisphere), # 
                                  interaction = "pairwise", adjust = "Bonferroni", infer = TRUE)

contra_high_N170_spec_hemi

##  Category_pairwise DuraBlock_pairwise Hemisphere_pairwise   estimate        SE       df   lower.CL  upper.CL t.ratio p.value
##  face - house      17_1 - 17_2        Left - Right        -0.3991541 0.4044924 17388.02 -1.3675945 0.5692862  -0.987  0.9713
##  face - house      17_1 - 200_2       Left - Right        -0.4144816 0.3231755  2014.65 -1.1888040 0.3598408  -1.283  0.5994
##  face - house      17_2 - 200_2       Left - Right        -0.0153275 0.3961737  6959.66 -0.9639887 0.9333338  -0.039  1.0000
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 3 estimates 
## P value adjustment: bonferroni method for 3 tests

summary(contra_high_N170_spec_hemi, delta = delta_null, level = .9)

##  Category_pairwise DuraBlock_pairwise Hemisphere_pairwise   estimate        SE       df   lower.CL  upper.CL t.ratio p.value
##  face - house      17_1 - 17_2        Left - Right        -0.3991541 0.4044924 17388.02 -1.2600007 0.4616924  -0.249  1.0000
##  face - house      17_1 - 200_2       Left - Right        -0.4144816 0.3231755  2014.65 -1.1026859 0.2737226  -0.265  1.0000
##  face - house      17_2 - 200_2       Left - Right        -0.0153275 0.3961737  6959.66 -0.8585705 0.8279156  -1.223  0.3318
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Conf-level adjustment: bonferroni method for 3 estimates 
## P value adjustment: bonferroni method for 3 tests 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

summary(contra_high_N170_spec_hemi, delta = delta_null, side = ">")

##  Category_pairwise DuraBlock_pairwise Hemisphere_pairwise   estimate        SE       df   lower.CL upper.CL t.ratio p.value
##  face - house      17_1 - 17_2        Left - Right        -0.3991541 0.4044924 17388.02 -1.2600007      Inf   0.249  1.0000
##  face - house      17_1 - 200_2       Left - Right        -0.4144816 0.3231755  2014.65 -1.1026859      Inf   0.265  1.0000
##  face - house      17_2 - 200_2       Left - Right        -0.0153275 0.3961737  6959.66 -0.8585705      Inf   1.223  0.3318
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 3 estimates 
## P value adjustment: bonferroni method for 3 tests 
## Statistics are tests of noninferiority with a threshold of 0.5 
## P values are right-tailed

summary(contra_high_N170_spec_hemi, delta = delta_null, side = "<")

##  Category_pairwise DuraBlock_pairwise Hemisphere_pairwise   estimate        SE       df lower.CL  upper.CL t.ratio p.value
##  face - house      17_1 - 17_2        Left - Right        -0.3991541 0.4044924 17388.02     -Inf 0.4616924  -2.223  0.0394
##  face - house      17_1 - 200_2       Left - Right        -0.4144816 0.3231755  2014.65     -Inf 0.2737226  -2.830  0.0071
##  face - house      17_2 - 200_2       Left - Right        -0.0153275 0.3961737  6959.66     -Inf 0.8279156  -1.301  0.2901
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 3 estimates 
## P value adjustment: bonferroni method for 3 tests 
## Statistics are tests of nonsuperiority with a threshold of 0.5 
## P values are left-tailed

5.4.2.9.2 Other results

Comparisons of P1 amplitudes for different durations and high confidence (without multiple comparison corrections):

summary(contra_high_N170[1:12], adjust = "none")

##  contrast     Hemisphere Category   estimate        SE       df   lower.CL   upper.CL t.ratio p.value
##  17_1 - 17_2  Left       face      0.6015598 0.1514381 18306.67  0.3047269  0.8983928   3.972  0.0001
##  17_1 - 200_2 Left       face      0.5576724 0.1272494 17686.68  0.3082511  0.8070938   4.383  <.0001
##  17_2 - 200_2 Left       face     -0.0438874 0.1636515 18181.18 -0.3646599  0.2768851  -0.268  0.7886
##  17_1 - 17_2  Right      face      0.9982659 0.1514036 18303.96  0.7015006  1.2950311   6.593  <.0001
##  17_1 - 200_2 Right      face      0.9114827 0.1270620 17334.05  0.6624283  1.1605370   7.174  <.0001
##  17_2 - 200_2 Right      face     -0.0867832 0.1635189 18079.17 -0.4072959  0.2337295  -0.531  0.5956
##  17_1 - 17_2  Left       house     0.9464568 0.2438305 18118.27  0.4685259  1.4243876   3.882  0.0001
##  17_1 - 200_2 Left       house    -0.9979058 0.2002644  6039.12 -1.3904955 -0.6053161  -4.983  <.0001
##  17_2 - 200_2 Left       house    -1.9443625 0.2336397 12454.50 -2.4023324 -1.4863927  -8.322  <.0001
##  17_1 - 17_2  Right      house     0.9440086 0.2430592 17107.44  0.4675877  1.4204296   3.884  0.0001
##  17_1 - 200_2 Right      house    -1.0585772 0.1938078  1752.20 -1.4386959 -0.6784584  -5.462  <.0001
##  17_2 - 200_2 Right      house    -2.0025858 0.2299383  5952.07 -2.4533482 -1.5518234  -8.709  <.0001
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95

summary(contra_high_N170[1:12], delta = delta_null, adjust = "none", level = .9)

##  contrast     Hemisphere Category   estimate        SE       df   lower.CL   upper.CL t.ratio p.value
##  17_1 - 17_2  Left       face      0.6015598 0.1514381 18306.67  0.3524537  0.8506660   0.671  0.7488
##  17_1 - 200_2 Left       face      0.5576724 0.1272494 17686.68  0.3483548  0.7669901   0.453  0.6748
##  17_2 - 200_2 Left       face     -0.0438874 0.1636515 18181.18 -0.3130839  0.2253091  -2.787  0.0027
##  17_1 - 17_2  Right      face      0.9982659 0.1514036 18303.96  0.7492165  1.2473152   3.291  0.9995
##  17_1 - 200_2 Right      face      0.9114827 0.1270620 17334.05  0.7024731  1.1204922   3.238  0.9994
##  17_2 - 200_2 Right      face     -0.0867832 0.1635189 18079.17 -0.3557617  0.1821953  -2.527  0.0058
##  17_1 - 17_2  Left       house     0.9464568 0.2438305 18118.27  0.5453708  1.3475427   1.831  0.9664
##  17_1 - 200_2 Left       house    -0.9979058 0.2002644  6039.12 -1.3273620 -0.6684496   2.486  0.9935
##  17_2 - 200_2 Left       house    -1.9443625 0.2336397 12454.50 -2.3286942 -1.5600309   6.182  1.0000
##  17_1 - 17_2  Right      house     0.9440086 0.2430592 17107.44  0.5441902  1.3438270   1.827  0.9661
##  17_1 - 200_2 Right      house    -1.0585772 0.1938078  1752.20 -1.3775312 -0.7396232   2.882  0.9980
##  17_2 - 200_2 Right      house    -2.0025858 0.2299383  5952.07 -2.3808595 -1.6243121   6.535  1.0000
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

contra_high_N170_spec.hemi <- contrast(emmeans(lmm.opt.N170.high.E205, ~ Category + DuraBlock + Hemisphere), # 
                                       interaction = "pairwise", adjust = "Bonferroni", infer = TRUE)

contra_high_N170_spec.hemi

##  Category_pairwise DuraBlock_pairwise Hemisphere_pairwise   estimate        SE       df   lower.CL  upper.CL t.ratio p.value
##  face - house      17_1 - 17_2        Left - Right        -0.3991541 0.4044924 17388.02 -1.3675945 0.5692862  -0.987  0.9713
##  face - house      17_1 - 200_2       Left - Right        -0.4144816 0.3231755  2014.65 -1.1888040 0.3598408  -1.283  0.5994
##  face - house      17_2 - 200_2       Left - Right        -0.0153275 0.3961737  6959.66 -0.9639887 0.9333338  -0.039  1.0000
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.95 
## Conf-level adjustment: bonferroni method for 3 estimates 
## P value adjustment: bonferroni method for 3 tests

summary(contra_high_N170_spec.hemi, delta = delta_null, level = .9)

##  Category_pairwise DuraBlock_pairwise Hemisphere_pairwise   estimate        SE       df   lower.CL  upper.CL t.ratio p.value
##  face - house      17_1 - 17_2        Left - Right        -0.3991541 0.4044924 17388.02 -1.2600007 0.4616924  -0.249  1.0000
##  face - house      17_1 - 200_2       Left - Right        -0.4144816 0.3231755  2014.65 -1.1026859 0.2737226  -0.265  1.0000
##  face - house      17_2 - 200_2       Left - Right        -0.0153275 0.3961737  6959.66 -0.8585705 0.8279156  -1.223  0.3318
## 
## Degrees-of-freedom method: satterthwaite 
## Confidence level used: 0.9 
## Conf-level adjustment: bonferroni method for 3 estimates 
## P value adjustment: bonferroni method for 3 tests 
## Statistics are tests of equivalence with a threshold of 0.5 
## P values are left-tailed

6 Appendix

6.1 Model selection procedure

For the analysis for every dependent variable, an optimal model was obtained by the following general steps:

1. Maximum Model: a maximum model with all fixed and random factors was built.
2. ZCP Model: a zero-correlated-parameter (zcp) model based on the maximum model is built. The only difference between zcp and maximum models is that the correlations between random effects are forced to be zero in the zcp model.
3. Reduced Model: the random effects which are not supported by the data will be removed from the zcp model. The function step(fit, fixed.reduce = FALSE) from library(lmerTest) is used for this step. The algorithm could be found here.
4. Extended Model: extending the reduced model with correlation parameters between the remaining random effects.
5. Pruning Model: pruning low correlation parameters. (Usually I don’t do this.)

More details about this whole process chould be found here: Bates, D., Kliegl, R., Vasishth, S., & Baayen, H. (2015). Parsimonious Mixed Models. Retrieved from http://arxiv.org/abs/1506.04967.

6.2 Stimuli specific effects

# 28 participants in total

resp.stim <- {
  clean_beha_205 %>% 
    # filter(Duration == "17", Resp == "1") %>% 
    mutate(isKey1 = as.numeric(Resp == "1"),
           Type_Category = paste(Type, Category, sep = "-")) %>% 
    group_by(Stimuli, Duration, Type_Category) %>% 
    summarize(avgCount = mean(isKey1)) 
  
}

ggplot(resp.stim, aes(x = Stimuli, y = avgCount, fill = Type_Category)) +
  geom_col() +
  facet_grid(Duration ~ .) +
  labs(title = "Averaged percentage of being responsed as 'Key 1' for each stimuli",
       y = "Percentage") +
  theme(axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),
        text = element_text(size = 10),
        axis.title = element_text(size = 16), 
        axis.text = element_text(size = 15), # the size of the texts in plot
        # axis.text.x = element_text(angle = 45, vjust = 0.5),
        legend.title=element_text(size=16),
        legend.text=element_text(size=16),
        # legend.position = "bottom",
        legend.key.width = unit(1.2, "cm"),
        plot.title = element_text(lineheight=.8, face="bold", size = 17),
        strip.text = element_text(face="bold", size=15, lineheight=5.0),
        strip.background = element_rect(fill="white", colour="white", size=1),
        strip.placement = "outside",
        legend.position = "bottom") 

resp.stim.1 <- {
  clean_beha_205 %>% 
    mutate(isKey1 = as.numeric(Resp == "1")) %>% 
    filter(Type == "intact", Category == "face") %>% 
    group_by(Stimuli, SubjCode, Duration) %>% 
    summarize(avgCount = mean(isKey1)) %>% 
    ungroup() 
}

resp.stim.1.17 <- {
  resp.stim.1 %>% 
    filter(Duration == "17") %>% 
    select(-Duration) %>% 
    spread(Stimuli, avgCount)
}

resp.stim.1.17 <- data.matrix(select(resp.stim.1.17, -SubjCode))

resp.stim.1.200 <- {
  resp.stim.1 %>% 
    filter(Duration == "200") %>% 
    select(-Duration) %>% 
    spread(Stimuli, avgCount)
}

resp.stim.1.200 <- data.matrix(select(resp.stim.1.200, -SubjCode))


library(plotly)

heatmap_17 <- plot_ly(z = data.matrix(resp.stim.1.17), type = "heatmap", name = "Figrename")
heatmap_200 <- plot_ly(z = data.matrix(resp.stim.1.200), type = "heatmap", name = "Figrename")

6.3 Gray plots for publication

6.3.1 Equivalence tests

gg_equi_bw

6.3.2 Behavioural responses

plot_two_response

6.3.3 P1 – all

p1_all_LinePlot_bw <- {
  ggplot(data = data.frame(emm.P1.E205), aes(y = emmean, x = Duration, group = Category)) +  # set the data, varialbes for x and y axises, and the variable for dividing data
    geom_point(aes(fill = Duration), size=4, shape=21, stroke=0) + # , alpha = .7
    scale_fill_manual(values = c("gray70", "black"), guide = "none") +
    geom_line(aes(linetype = Category), size = .85) +  # position = "dodge", alpha = .7
    scale_linetype_manual(values=c("solid", "dashed")) +
    geom_errorbar(mapping = aes(ymin = lower.CL, ymax = upper.CL), linetype = 1,  # set the error bar
                  show.legend = FALSE, width = 0.15, alpha = .5) + # ,                   position = position_dodge(width=0.9)
    facet_grid(Type ~ Hemisphere, # switch = "x", 
               labeller = labeller(Hemisphere = hemiLabel)) +
    scale_y_reverse(limits = ylimit_p1) +  # set the limit for y axis
    # scale_y_continuous(expand= c(0, 0)) +  # remove the space between columns and x axis
    labs(x = "Duration (ms)", y = expression(paste("P1 Amplitudes (", mu, "V)")), color = "Category", linetype = "Category") +  # set the names for main, x and y axises title = "", 
    # scale_x_discrete(labels = c("left", "right")) +
    # geom_text(label = c("", "", "", "", "***", "", "", ""), color = "red", size = 6, nudge_y = 0.5, nudge_x = 0.5) + # add starts to the significant columns
    theme_bw() +
    erp_theme +
    theme(
      legend.position = c(.5, .5),
      legend.title=element_text(size=10), 
      legend.text=element_text(size=10),
    ) +
    guides(linetype = guide_legend(title.position = "left", nrow = 1),
           color = guide_legend(title.position = "left", nrow = 1)) +
    NULL
}

p1_all_LinePlot_bw

6.3.4 N170 – all

n170_all_LinePlot_bw <- {
  ggplot(data = data.frame(emm.N170.E205), aes(y = emmean, x = Duration, group = Category)) +  # set the data, varialbes for x and y axises, and the variable for dividing data
    geom_point(aes(fill = Duration), size=4, shape=21, stroke=0) + # , alpha = .7
    scale_fill_manual(values = c("gray70", "black"), guide = "none") +
    geom_line(aes(linetype = Category), size = .85) +  # position = "dodge", alpha = .7
    scale_linetype_manual(values=c("solid", "dashed")) +
    geom_errorbar(mapping = aes(ymin = lower.CL, ymax = upper.CL), linetype = 1,  # set the error bar
                  show.legend = FALSE, width = 0.15, alpha = .5) + # ,                   position = position_dodge(width=0.9)
    facet_grid(Type ~ Hemisphere, # switch = "x",
               labeller = labeller(Hemisphere = hemiLabel)) +
    scale_y_reverse(limits = ylimit_n170, breaks = seq(1, -5, -1)) +  # set the limit for y axis
    # scale_y_continuous(expand= c(0, 0)) +  # remove the space between columns and x axis
    labs(x = "Duration (ms)", y = expression(paste("N170 Amplitudes (", mu, "V)")), color = "Category", linetype = "Category") +  # set the names for main, x and y axises title = "", 
    # scale_x_discrete(labels = c("left", "right")) +
    # geom_text(label = c("", "", "", "", "***", "", "", ""), color = "red", size = 6, nudge_y = 0.5, nudge_x = 0.5) + # add starts to the significant columns
    theme_bw() +
    erp_theme +
    theme(
      legend.position = c(.5, .5),
      legend.title=element_text(size=10), 
      legend.text=element_text(size=10),
    ) +
    guides(linetype = guide_legend(title.position = "left", nrow = 1),
           color = guide_legend(title.position = "left", nrow = 1))
}

n170_all_LinePlot_bw

6.3.5 P1 – con

p1_con_LinePlot_bw = {
  ggplot(data = data.frame(emm.P1.SC.E205), aes(y = emmean, x = DuraConf, group = Category)) +  # set the data, varialbes for x and y axises, and the variable for dividing data
    geom_point(aes(fill = DuraConf), size=4, shape=21, stroke=0) +
    scale_fill_manual(values = c("gray75", "gray60", "gray35", "black"), guide = "none") +
    geom_line(aes(linetype = Category), size = .85) +  # position = "dodge", alpha = .7
    scale_linetype_manual(values=c("solid", "dashed")) +
    geom_errorbar(mapping = aes(ymin = lower.CL, ymax = upper.CL), linetype = 1,  # set the error bar
                  show.legend = FALSE, width = 0.15, alpha = .5) + # ,                   position = position_dodge(width=0.9)
    facet_grid(. ~ Hemisphere, # switch = "x",
               labeller = labeller(Hemisphere = hemiLabel)) +
    scale_y_reverse(limits = ylimit_p1) +  # set the limit for y axis
    scale_x_discrete(labels = duraconf.labels) +
    # scale_y_continuous(expand= c(0, 0)) +  # remove the space between columns and x axis
    labs(x = "Duration and Subjective Confidence", y = expression(paste("P1 Amplitudes (", mu, "V)")), fill = "Category") +  # set the names for main, x and y axises title = "", 
    theme_bw() +
    erp_theme + 
    theme(
      axis.title.x = element_text(margin = margin(t = 10, r = 0, b = 0, l = 0)),
      legend.margin = margin(t = -10),
      legend.title=element_text(size=12), 
      legend.text=element_text(size=12),
    )
}

p1_con_LinePlot_bw

6.3.6 N170 – con

n170_con_LinePlot_bw = {
  ggplot(data = data.frame(emm.N170.SC.E205), aes(y = emmean, x = DuraConf, group = Category)) +  # set the data, varialbes for x and y axises, and the variable for dividing data
    geom_point(aes(fill = DuraConf), size=4, shape=21, stroke=0) +
    scale_fill_manual(values = c("gray75", "gray60", "gray35", "black"), guide = "none") +
    geom_line(aes(linetype = Category), size = .85) +  # position = "dodge", alpha = .7
    scale_linetype_manual(values=c("solid", "dashed")) +
    geom_errorbar(mapping = aes(ymin = lower.CL, ymax = upper.CL), linetype = 1,  # set the error bar
                  show.legend = FALSE, width = 0.15, alpha = .5) + # ,                   position = position_dodge(width=0.9)
    facet_grid(. ~ Hemisphere, # switch = "x",
               labeller = labeller(Hemisphere = hemiLabel)) +
    scale_y_reverse(limits = ylimit_n170, breaks = seq(1, -5, -1)) +  # set the limit for y axis
    scale_x_discrete(labels = duraconf.labels) +
    # scale_y_continuous(expand= c(0, 0)) +  # remove the space between columns and x axis
    labs(x = "Duration and Subjective Confidence", y = expression(paste("N170 Amplitudes (", mu, "V)")), fill = "Category") +  # set the names for main, x and y axises title = "", 
    theme_bw() +
    erp_theme +
    theme(
      axis.title.x = element_text(margin = margin(t = 10, r = 0, b = 0, l = 0)),
      legend.margin = margin(t = -10),
      legend.title=element_text(size=12), 
      legend.text=element_text(size=12),
    )
}

n170_con_LinePlot_bw

6.3.7 P1 – high

p1_high_LinePlot_bw = {
  ggplot(data = data.frame(emm.P1.high.E205), aes(y = emmean, x = DuraBlock, group = Category)) +  # set the data, variables for x and y axises, and the variable for dividing data
    geom_point(aes(fill = DuraBlock), size=4, shape=21, stroke=0) +
    scale_fill_manual(values = c("gray60", "gray35", "black"), guide = "none") +
    geom_line(aes(linetype = Category), size = .85) +  # position = "dodge", alpha = .7
    scale_linetype_manual(values=c("solid", "dashed")) +
    geom_errorbar(mapping = aes(ymin = lower.CL, ymax = upper.CL), linetype = 1,  # set the error bar
                  show.legend = FALSE, width = 0.15, alpha = .5) + # ,                   position = position_dodge(width=0.9)
    facet_grid(. ~ Hemisphere, # switch = "x",
               labeller = labeller(Hemisphere = hemiLabel)) +
    scale_y_reverse(limits = ylimit_p1) +  # set the limit for y axis
    scale_x_discrete(labels = durablock.labels) +
    # scale_y_continuous(expand= c(0, 0)) +  # remove the space between columns and x axis
    labs(x = "Duration and Halves", y = expression(paste("P1 Amplitudes (", mu, "V)")), fill = "Category") +  # set the names for main, x and y axises title = "", 
    theme_bw() +
    erp_theme +
    theme(
      axis.title.x = element_text(margin = margin(t = 10, r = 0, b = 0, l = 0)),
      legend.margin = margin(t = -10),
      legend.title=element_text(size=12), 
      legend.text=element_text(size=12),
    )
}

p1_high_LinePlot_bw

6.3.8 N170 – high

n170_high_LinePlot_bw = {
  ggplot(data = data.frame(emm.N170.high.E205), aes(y = emmean, x = DuraBlock, group = Category)) +  # set the data, varialbes for x and y axises, and the variable for dividing data
    geom_point(aes(fill = DuraBlock), size=4, shape=21, stroke=0) +
    scale_fill_manual(values = c("gray60", "gray35", "black"), guide = "none") +
    geom_line(aes(linetype = Category), size = .85) +  # position = "dodge", alpha = .7
    scale_linetype_manual(values=c("solid", "dashed")) +
    geom_errorbar(mapping = aes(ymin = lower.CL, ymax = upper.CL), linetype = 1,  # set the error bar
                  show.legend = FALSE, width = 0.15, alpha = .5) + # ,                   position = position_dodge(width=0.9)
    facet_grid(. ~ Hemisphere, # switch = "x",
               labeller = labeller(Hemisphere = hemiLabel)) +
    scale_y_reverse(limits = ylimit_n170, breaks = seq(1, -5, -1)) +  # set the limit for y axis
    scale_x_discrete(labels = durablock.labels) +
    # scale_y_continuous(expand= c(0, 0)) +  # remove the space between columns and x axis
    labs(x = "Duration and Halves", y = expression(paste("N170 Amplitudes (", mu, "V)")), fill = "Category") +  # set the names for main, x and y axises title = "", 
    theme_bw() +
    erp_theme +
    theme(
      axis.title.x = element_text(margin = margin(t = 10, r = 0, b = 0, l = 0)),
      legend.margin = margin(t = -10),
      legend.title=element_text(size=12), 
      legend.text=element_text(size=12),
    )
}

n170_high_LinePlot_bw

6.3.9 Save plots

# filetype <- "pdf"
# ggsave(paste0("equivalence_test_bw.", filetype), gg_equi_bw, width = 8, height = 4.7)
# ggsave(paste0('plot_two_response_bw.', filetype), plot_two_response, width = 10, height = 5)
# ggsave(paste0('P1_all_pub_bw.', filetype), p1_all_LinePlot_bw, width = 6, height = 5)
# ggsave(paste0('N170_all_pub_bw.', filetype), n170_all_LinePlot_bw, width = 6, height = 5)
# ggsave(paste0('P1_con_pub_bw.', filetype), p1_con_LinePlot_bw, width = 7, height = 5)
# ggsave(paste0('N170_con_pub_bw.', filetype), n170_con_LinePlot_bw, width = 7, height = 5)
# ggsave(paste0('p1_high_pub_bw.', filetype), p1_high_LinePlot_bw, width = 7, height = 4.5)
# ggsave(paste0('n170_high_pub_bw.', filetype), n170_high_LinePlot_bw, width = 7, height = 4.5)

7 Versions of packages used

# rstudioapi::versionInfo()
sessionInfo()

## R version 4.0.5 (2021-03-31)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Mojave 10.14.5
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] plotly_4.9.4.1  emmeans_1.6.3   lmerTest_3.1-3  lme4_1.1-26     Matrix_1.3-2    magrittr_2.0.1  forcats_0.5.1   stringr_1.4.0   dplyr_1.0.5     purrr_0.3.4     readr_1.4.0     tidyr_1.1.3     tibble_3.1.1    ggplot2_3.3.5   tidyverse_1.3.1
## 
## loaded via a namespace (and not attached):
##  [1] nlme_3.1-152        fs_1.5.0            lubridate_1.7.10    httr_1.4.2          numDeriv_2016.8-1.1 tools_4.0.5         backports_1.2.1     utf8_1.2.1          R6_2.5.0            lazyeval_0.2.2      DBI_1.1.0           colorspace_1.4-1    withr_2.4.2         tidyselect_1.1.0    curl_4.3.1          compiler_4.0.5      cli_2.5.0           rvest_1.0.0         xml2_1.3.2          labeling_0.4.2      scales_1.1.1        mvtnorm_1.1-1       digest_0.6.27       foreign_0.8-81      minqa_1.2.4         rmarkdown_2.7       rio_0.5.26          pkgconfig_2.0.3     htmltools_0.5.1.1   dbplyr_2.1.1        highr_0.9           htmlwidgets_1.5.3   rlang_0.4.11        readxl_1.3.1        rstudioapi_0.13     farver_2.0.3        generics_0.1.0      jsonlite_1.7.2      crosstalk_1.1.0.1   zip_2.1.1           car_3.0-10          Rcpp_1.0.6          munsell_0.5.0       fansi_0.4.2         abind_1.4-5         lifecycle_1.0.0     stringi_1.6.1       yaml_2.2.1          carData_3.0-4       MASS_7.3-53.1       plyr_1.8.6          grid_4.0.5          crayon_1.4.1        lattice_0.20-41     cowplot_1.1.1       haven_2.3.1         splines_4.0.5       hms_1.0.0           knitr_1.33          pillar_1.6.0        ggpubr_0.4.0        uuid_0.1-4          boot_1.3-27         estimability_1.3    ggsignif_0.6.1      reprex_2.0.0        glue_1.4.2          evaluate_0.14       data.table_1.14.0   modelr_0.1.8        vctrs_0.3.8         nloptr_1.2.2.2      cellranger_1.1.0    gtable_0.3.0       
## [75] assertthat_0.2.1    xfun_0.22           openxlsx_4.2.3      xtable_1.8-4        broom_0.7.6         coda_0.19-4         rstatix_0.7.0       viridisLite_0.3.0   statmod_1.4.35      ellipsis_0.3.2      xaringanExtra_0.4.0